id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5c0c37b90e9c180a75fa7d09218f3930b3b33de642aee8c39728246017d3d554 | [
"arxiv",
"semantic_scholar"
] | SPARK: Synergistic Policy And Reward Co-Evolving Framework | Recent Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) increasingly use Reinforcement Learning (RL) for post-pretraining, such as RL with Verifiable Rewards (RLVR) for objective tasks and RL from Human Feedback (RLHF) for subjective tasks. However, RLHF incurs high costs and potential reward-polic... | [
"Ziyu Liu",
"Yuhang Zang",
"Shengyuan Ding",
"Yuhang Cao",
"Xiaoyi Dong",
"Haodong Duan",
"Dahua Lin",
"Jiaqi Wang"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-26T00:00:00 | https://arxiv.org/abs/2509.22624 | https://arxiv.org/pdf/2509.22624v1 | 2509.22624 | 10.48550/arXiv.2509.22624 | 7 | 0 | true | https://github.com/InternLM/Spark | arXiv.org | 0.4852 |
d131ab1547596287013b6a4eab582f3b9a4045e49e21e5bb8284225ae0393785 | [
"arxiv",
"semantic_scholar"
] | GRPO is Secretly a Process Reward Model | Process reward models (PRMs) allow for fine-grained credit assignment in reinforcement learning (RL), and seemingly contrast with outcome reward models (ORMs), which assign a single reward to an entire trajectory. However, we provide theoretical proof in this work that the Group Relative Policy Optimization (GRPO) RL a... | [
"Michael Sullivan",
"Alexander Koller"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-25T00:00:00 | https://arxiv.org/abs/2509.21154 | https://arxiv.org/pdf/2509.21154v4 | 2509.21154 | 10.48550/arXiv.2509.21154 | 4 | 0 | false | null | arXiv.org | 0.3128 |
01c59db9ecbe6fa44caf0f4469c0124517a18284f1618a8f5f2798d6a2f86fd3 | [
"arxiv",
"semantic_scholar"
] | What Fundamental Structure in Reward Functions Enables Efficient Sparse-Reward Learning? | Sparse-reward reinforcement learning (RL) remains fundamentally hard: without structure, any agent needs $Ω(|\mathcal{S}||\mathcal{A}|/p)$ samples to recover rewards. We introduce Policy-Aware Matrix Completion (PAMC) as a first concrete step toward a structural reward learning framework. Our key idea is to exploit app... | [
"Ibne Farabi Shihab",
"Sanjeda Akter",
"Anuj Sharma"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-09-04T00:00:00 | https://arxiv.org/abs/2509.03790 | https://arxiv.org/pdf/2509.03790v2 | 2509.03790 | 10.48550/arXiv.2509.03790 | 1 | 0 | false | null | arXiv.org | 0.2888 |
3abdc739bc0b7070e2158b7650f636230fb2a86a0307fb2fb4b9ad7f12889c0a | [
"arxiv",
"semantic_scholar"
] | SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences | Uniform-reward reinforcement learning from human feedback (RLHF), which trains a single reward model to represent the preferences of all annotators, fails to capture the diversity of opinions across sub-populations, inadvertently favoring dominant groups. The state-of-the-art, MaxMin-RLHF, addresses this by learning gr... | [
"Arpan Mukherjee",
"Marcello Bullo",
"Deniz Gündüz"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-09-03T00:00:00 | https://arxiv.org/abs/2509.03672 | https://arxiv.org/pdf/2509.03672v1 | 2509.03672 | 10.48550/arXiv.2509.03672 | 0 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.2876 |
e5702c497b56606ff5504820dcad2171002f137dc8b7ee5166f9080f182eebd5 | [
"arxiv",
"semantic_scholar"
] | GRAM-R$^2$: Self-Training Generative Foundation Reward Models for Reward Reasoning | Significant progress in reward modeling over recent years has been driven by a paradigm shift from task-specific designs towards generalist reward models. Despite this trend, developing effective reward models remains a fundamental challenge: the heavy reliance on large-scale labeled preference data. Pre-training on ab... | [
"Chenglong Wang",
"Yongyu Mu",
"Hang Zhou",
"Yifu Huo",
"Ziming Zhu",
"Jiali Zeng",
"Murun Yang",
"Bei Li",
"Xiaoyang Hao",
"Chunliang Zhang",
"Fandong Meng",
"Jingbo Zhu",
"Tong Xiao"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-09-02T00:00:00 | https://arxiv.org/abs/2509.02492 | https://arxiv.org/pdf/2509.02492v3 | 2509.02492 | 10.48550/arXiv.2509.02492 | 5 | 1 | false | null | AAAI Conference on Artificial Intelligence | 0.2865 |
31f06e60ac7f248fc0e4e2c2f898fda43c13977dd2bc09119a679df15e8d2556 | [
"arxiv",
"semantic_scholar"
] | Counterfactual Reward Model Training for Bias Mitigation in Multimodal Reinforcement Learning | In reinforcement learning with human feedback (RLHF), reward models can efficiently learn and amplify latent biases within multimodal datasets, which can lead to imperfect policy optimization through flawed reward signals and decreased fairness. Bias mitigation studies have often applied passive constraints, which can ... | [
"Sheryl Mathew",
"N Harshit"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-08-27T00:00:00 | https://arxiv.org/abs/2508.19567 | https://arxiv.org/pdf/2508.19567v1 | 2508.19567 | 10.48550/arXiv.2508.19567 | 0 | 0 | false | null | arXiv.org | 0.2796 |
c9b19869f8cfdbe093f3650876eb40396c1fc8bced8787cd323eafa178fbf66b | [
"arxiv",
"semantic_scholar"
] | Fusing Rewards and Preferences in Reinforcement Learning | We present Dual-Feedback Actor (DFA), a reinforcement learning algorithm that fuses both individual rewards and pairwise preferences (if available) into a single update rule. DFA uses the policy's log-probabilities directly to model the preference probability, avoiding a separate reward-modeling step. Preferences can b... | [
"Sadegh Khorasani",
"Saber Salehkaleybar",
"Negar Kiyavash",
"Matthias Grossglauser"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-08-15T00:00:00 | https://arxiv.org/abs/2508.11363 | https://arxiv.org/pdf/2508.11363v1 | 2508.11363 | 10.48550/arXiv.2508.11363 | 2 | 0 | false | null | arXiv.org | 0.2658 |
663b5ef7858511b7bc7a8fdaa54efef7ef36cde7c7cbdfa663908cc941b4bb2a | [
"arxiv",
"semantic_scholar"
] | Interpretable Reward Model via Sparse Autoencoder | Large language models (LLMs) have been widely deployed across numerous fields. Reinforcement Learning from Human Feedback (RLHF) leverages reward models (RMs) as proxies for human preferences to align LLM behaviors with human values, making the accuracy, reliability, and interpretability of RMs critical for effective a... | [
"Shuyi Zhang",
"Wei Shi",
"Sihang Li",
"Jiayi Liao",
"Hengxing Cai",
"Xiang Wang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-08-12T00:00:00 | https://arxiv.org/abs/2508.08746 | https://arxiv.org/pdf/2508.08746v5 | 2508.08746 | 10.48550/arXiv.2508.08746 | 14 | 1 | true | https://github.com/schrieffer-z/sarm | AAAI Conference on Artificial Intelligence | 0.4055 |
b799dd705b4715541bc6e7d2cf3a69a580dec74f033ad0c213b6877e76fb430a | [
"arxiv",
"semantic_scholar"
] | ReCode: Reinforcing Code Generation with Reasoning-Process Rewards | In practice, rigorous reasoning is often a key driver of correct code, while Reinforcement Learning (RL) for code generation often neglects optimizing reasoning quality. Bringing process-level supervision into RL is appealing, but it faces two challenges. First, training reliable reward models to assess reasoning quali... | [
"Lishui Fan",
"Yu Zhang",
"Mouxiang Chen",
"Zhongxin Liu"
] | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-08-07T00:00:00 | https://arxiv.org/abs/2508.05170 | https://arxiv.org/pdf/2508.05170v3 | 2508.05170 | null | 21 | 3 | false | null | null | 0.3356 |
79c008908f6d3ea0df9c8d715d23a747f4a412f187eccbe80eee972cdeb29bf4 | [
"arxiv",
"semantic_scholar"
] | Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap | Aligning large language models (LLMs) with human preferences is a critical challenge in AI research. While methods like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) are widely used, they often rely on large, costly preference datasets. The current work lacks methods for hig... | [
"Xuan Qi",
"Rongwu Xu",
"Zhijing Jin"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-08-06T00:00:00 | https://arxiv.org/abs/2508.04149 | https://arxiv.org/pdf/2508.04149v2 | 2508.04149 | 10.48550/arXiv.2508.04149 | 7 | 0 | true | https://github.com/Difficulty-Based-Preference-Data-Select/Difficulty-Based-Preference-Data-Select | arXiv.org | 0.3949 |
97da6da7b3c1d7f1733121c49ef497c79a0355e043107cc37e555d528e7c87fd | [
"arxiv",
"semantic_scholar"
] | Inferring Reward Machines and Transition Machines from Partially Observable Markov Decision Processes | Partially Observable Markov Decision Processes (POMDPs) are fundamental to many real-world applications. Although reinforcement learning (RL) has shown success in fully observable domains, learning policies from traces in partially observable environments remains challenging due to non-Markovian observations. Inferring... | [
"Yuly Wu",
"Jiamou Liu",
"Libo Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-08-03T00:00:00 | https://arxiv.org/abs/2508.01947 | https://arxiv.org/pdf/2508.01947v1 | 2508.01947 | 10.48550/arXiv.2508.01947 | 0 | 0 | true | https://github.com/sousoura/Inferring-Reward-Machines-and-Transition-Machines-from-POMDP.git | arXiv.org | 0.3896 |
7491d5683c99bfeefe018fabf618c77bffcbc329edfcb0acaade8612acea86df | [
"arxiv",
"semantic_scholar"
] | The Bidirectional Process Reward Model | Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps within a solution trajectory, have emerged as a promising approach to enhance the reasoning quality of Large Language Models (LLMs). However, most existing PRMs rely on a unidirectional left-to-right (L2R) evaluation scheme, ... | [
"Lingyin Zhang",
"Jun Gao",
"Xiaoxue Ren",
"Ziqiang Cao"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-08-03T00:00:00 | https://arxiv.org/abs/2508.01682 | https://arxiv.org/pdf/2508.01682v2 | 2508.01682 | 10.48550/arXiv.2508.01682 | 2 | 0 | false | null | arXiv.org | 0.2521 |
9587517d1a250d78a2ea7100894b5ca504acac6037a31a55e63166552dc8afac | [
"arxiv",
"semantic_scholar"
] | Policy Learning from Large Vision-Language Model Feedback without Reward Modeling | Offline reinforcement learning (RL) provides a powerful framework for training robotic agents using pre-collected, suboptimal datasets, eliminating the need for costly, time-consuming, and potentially hazardous online interactions. This is particularly useful in safety-critical real-world applications, where online dat... | [
"Tung M. Luu",
"Donghoon Lee",
"Younghwan Lee",
"Chang D. Yoo"
] | [
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2025-07-31T00:00:00 | https://arxiv.org/abs/2507.23391 | https://arxiv.org/pdf/2507.23391v1 | 2507.23391 | 10.1109/IROS60139.2025.11246902 | 6 | 0 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.2486 |
fa115d47a00a5a7d7933f94e8466bc657c98c35867aa7921e91d0fdaaf043be5 | [
"arxiv",
"semantic_scholar"
] | Multimodal LLMs as Customized Reward Models for Text-to-Image Generation | We introduce LLaVA-Reward, an efficient reward model designed to automatically evaluate text-to-image (T2I) generations across multiple perspectives, leveraging pretrained multimodal large language models (MLLMs). Existing MLLM-based approaches require instruction-following data for supervised fine-tuning and evaluate ... | [
"Shijie Zhou",
"Ruiyi Zhang",
"Huaisheng Zhu",
"Branislav Kveton",
"Yufan Zhou",
"Jiuxiang Gu",
"Jian Chen",
"Changyou Chen"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-07-28T00:00:00 | https://arxiv.org/abs/2507.21391 | https://arxiv.org/pdf/2507.21391v2 | 2507.21391 | 10.1109/ICCV51701.2025.01826 | 9 | 1 | true | https://github.com/sjz5202/LLaVA-Reward | IEEE International Conference on Computer Vision | 0.379 |
138ff77d0bcb41dc48703732472de43dd9ea35b6c8f28d78370fd9bf23d32cae | [
"arxiv",
"semantic_scholar"
] | Dynamic and Generalizable Process Reward Modeling | Process Reward Models (PRMs) are crucial for guiding Large Language Models (LLMs) in complex scenarios by providing dense reward signals. However, existing PRMs primarily rely on heuristic approaches, which struggle with cross-domain generalization. While LLM-as-judge has been proposed to provide generalized rewards, c... | [
"Zhangyue Yin",
"Qiushi Sun",
"Zhiyuan Zeng",
"Qinyuan Cheng",
"Xipeng Qiu",
"Xuanjing Huang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-07-23T00:00:00 | https://arxiv.org/abs/2507.17849 | https://arxiv.org/pdf/2507.17849v1 | 2507.17849 | 10.18653/v1/2025.acl-long.212 | 16 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.3076 |
2b71b255fd249af8aec4039afdd3d9827364095cd0504aa3b0f21f45a32a09bd | [
"arxiv",
"semantic_scholar"
] | Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback | Reinforcement Learning from Human Feedback (RLHF) allows us to train models, such as language models (LMs), to follow complex human preferences. In RLHF for LMs, we first train an LM using supervised fine-tuning, sample pairs of responses, obtain human feedback, and use the resulting data to train a reward model (RM). ... | [
"Johannes Ackermann",
"Takashi Ishida",
"Masashi Sugiyama"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-07-21T00:00:00 | https://arxiv.org/abs/2507.15507 | https://arxiv.org/pdf/2507.15507v1 | 2507.15507 | 10.48550/arXiv.2507.15507 | 5 | 0 | true | https://github.com/JohannesAck/OffPolicyCorrectedRewardModeling | arXiv.org | 0.3666 |
e68027ef6d0bcf43e1562e068b88d7404d53e4e19c5a178ab7c3ab4970c4867c | [
"arxiv",
"semantic_scholar"
] | Tiny Reward Models | Large decoder-based language models have become the dominant architecture for reward modeling in reinforcement learning from human feedback (RLHF). However, as reward models are increasingly deployed in test-time strategies, their inference costs become a growing concern. We present TinyRM, a family of small, bidirecti... | [
"Sarah Pan"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-14T00:00:00 | https://arxiv.org/abs/2507.09973 | https://arxiv.org/pdf/2507.09973v1 | 2507.09973 | 10.48550/arXiv.2507.09973 | 0 | 0 | false | null | arXiv.org | 0.2292 |
bbbf858be2ae361da6e2a4fb1bfe371efe19ee7defcdf3a280189dd5d35224f7 | [
"arxiv",
"semantic_scholar"
] | Recursive Reward Aggregation | In reinforcement learning (RL), aligning agent behavior with specific objectives typically requires careful design of the reward function, which can be challenging when the desired objectives are complex. In this work, we propose an alternative approach for flexible behavior alignment that eliminates the need to modify... | [
"Yuting Tang",
"Yivan Zhang",
"Johannes Ackermann",
"Yu-Jie Zhang",
"Soichiro Nishimori",
"Masashi Sugiyama"
] | [
"cs.LG",
"math.CT"
] | [
"Computer Science",
"Mathematics"
] | 2025-07-11T00:00:00 | https://arxiv.org/abs/2507.08537 | https://arxiv.org/pdf/2507.08537v2 | 2507.08537 | 10.48550/arXiv.2507.08537 | 3 | 0 | false | null | arXiv.org | 0.2257 |
55d62268686e902f42d2faf95ac5361cec21cd6eb8a73ecdd214f22d48014d3e | [
"arxiv",
"semantic_scholar"
] | Bradley-Terry and Multi-Objective Reward Modeling Are Complementary | Reward models trained on human preference data have demonstrated strong effectiveness in aligning Large Language Models (LLMs) with human intent under the framework of Reinforcement Learning from Human Feedback (RLHF). However, RLHF remains vulnerable to reward hacking, where the policy exploits imperfections in the re... | [
"Zhiwei Zhang",
"Hui Liu",
"Xiaomin Li",
"Zhenwei Dai",
"Jingying Zeng",
"Fali Wang",
"Minhua Lin",
"Ramraj Chandradevan",
"Zhen Li",
"Chen Luo",
"Xianfeng Tang",
"Qi He",
"Suhang Wang"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-07-10T00:00:00 | https://arxiv.org/abs/2507.07375 | https://arxiv.org/pdf/2507.07375v1 | 2507.07375 | 10.48550/arXiv.2507.07375 | 7 | 0 | false | null | arXiv.org | 0.2258 |
9b3ac64ce71b6b84af6b5c71c4334a3aa83a892c2371c83980a89c413e268135 | [
"arxiv",
"semantic_scholar"
] | Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling | Reward modeling (RM), which captures human preferences to align large language models (LLMs), is increasingly employed in tasks such as model finetuning, response filtering, and ranking. However, due to the inherent complexity of human preferences and the limited coverage of available datasets, reward models often fail... | [
"Pankayaraj Pathmanathan",
"Furong Huang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-07-08T00:00:00 | https://arxiv.org/abs/2507.06419 | https://arxiv.org/pdf/2507.06419v3 | 2507.06419 | null | 2 | 0 | false | null | ACL 2026 Main Conference [Oral] | 0.2223 |
d07a0744583b2b3a36917654c542f44f5d6e8941a21b0c911971da2fc2100c84 | [
"arxiv",
"semantic_scholar"
] | Interpretable Reward Modeling with Active Concept Bottlenecks | We introduce Concept Bottleneck Reward Models (CB-RM), a reward modeling framework that enables interpretable preference learning through selective concept annotation. Unlike standard RLHF methods that rely on opaque reward functions, CB-RM decomposes reward prediction into human-interpretable concepts. To make this fr... | [
"Sonia Laguna",
"Katarzyna Kobalczyk",
"Julia E. Vogt",
"Mihaela Van der Schaar"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-07-07T00:00:00 | https://arxiv.org/abs/2507.04695 | https://arxiv.org/pdf/2507.04695v2 | 2507.04695 | 10.48550/arXiv.2507.04695 | 1 | 0 | false | null | arXiv.org | 0.2211 |
1643c07009d41a7ebac6763a7c0d6743e5c81eff96f3d28994761bc055c04600 | [
"arxiv",
"semantic_scholar"
] | Pre-Trained Policy Discriminators are General Reward Models | We offer a novel perspective on reward modeling by formulating it as a policy discriminator, which quantifies the difference between two policies to generate a reward signal, guiding the training policy towards a target policy with desired behaviors. Based on this conceptual insight, we propose a scalable pre-training ... | [
"Shihan Dou",
"Shichun Liu",
"Yuming Yang",
"Yicheng Zou",
"Yunhua Zhou",
"Shuhao Xing",
"Chenhao Huang",
"Qiming Ge",
"Demin Song",
"Haijun Lv",
"Songyang Gao",
"Chengqi Lv",
"Enyu Zhou",
"Honglin Guo",
"Zhiheng Xi",
"Wenwei Zhang",
"Qipeng Guo",
"Qi Zhang",
"Xipeng Qiu",
"Xua... | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-07-07T00:00:00 | https://arxiv.org/abs/2507.05197 | https://arxiv.org/pdf/2507.05197v2 | 2507.05197 | 10.48550/arXiv.2507.05197 | 12 | 3 | false | null | arXiv.org | 0.301 |
1d61232ac9a584be3218c1e8dddc1e3e6ab9c921fd9db0785acb3c64118334e1 | [
"arxiv",
"semantic_scholar"
] | ARF-RLHF: Adaptive Reward-Following for RLHF through Emotion-Driven Self-Supervision and Trace-Biased Dynamic Optimization | Current RLHF methods such as PPO and DPO typically reduce human preferences to binary labels, which are costly to obtain and too coarse to reflect individual variation. We observe that expressions of satisfaction and dissatisfaction follow stable linguistic patterns across users, indicating that more informative superv... | [
"YuXuan Zhang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-03T00:00:00 | https://arxiv.org/abs/2507.03069 | https://arxiv.org/pdf/2507.03069v3 | 2507.03069 | 10.48550/arXiv.2507.03069 | 0 | 0 | false | null | arXiv.org | 0.2166 |
1be2e97a46581870f7e0c192880e517f768ec40d59f45e67cd9feaad0058ddee | [
"arxiv",
"semantic_scholar"
] | Uncertainty-aware Reward Design Process | Designing effective reward functions is a cornerstone of reinforcement learning (RL), yet it remains a challenging process due to the inefficiencies and inconsistencies inherent in conventional reward engineering methodologies. Recent advances have explored leveraging large language models (LLMs) to automate reward fun... | [
"Yang Yang",
"Xiaolu Zhou",
"Bosong Ding",
"Miao Xin"
] | [
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2025-07-03T00:00:00 | https://arxiv.org/abs/2507.02256 | https://arxiv.org/pdf/2507.02256v1 | 2507.02256 | 10.48550/arXiv.2507.02256 | 0 | 0 | false | null | null | 0.1378 |
952e485cff5ec38f2a0471bb6aa7c33a69ba9276c49e56db4a5d9aa9e3925c68 | [
"arxiv",
"semantic_scholar"
] | Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy | Despite the critical role of reward models (RMs) in Reinforcement Learning from Human Feedback (RLHF), current state-of-the-art open RMs perform poorly on most existing evaluation benchmarks, failing to capture nuanced human preferences. We hypothesize that this brittleness stems primarily from limitations in preferenc... | [
"Chris Yuhao Liu",
"Liang Zeng",
"Yuzhen Xiao",
"Jujie He",
"Jiacai Liu",
"Chaojie Wang",
"Rui Yan",
"Wei Shen",
"Fuxiang Zhang",
"Jiacheng Xu",
"Yang Liu",
"Yahui Zhou"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-07-02T00:00:00 | https://arxiv.org/abs/2507.01352 | https://arxiv.org/pdf/2507.01352v3 | 2507.01352 | 10.48550/arXiv.2507.01352 | 154 | 17 | false | null | arXiv.org | 0.6276 |
8edd344b927941def2ef488153ca17681c0be3af8228070e2686718ae436371d | [
"arxiv",
"semantic_scholar"
] | Activation Reward Models for Few-Shot Model Alignment | Aligning Large Language Models (LLMs) and Large Multimodal Models (LMMs) to human preferences is a central challenge in improving the quality of the models' generative outputs for real-world applications. A common approach is to use reward modeling to encode preferences, enabling alignment via post-training using reinf... | [
"Tianning Chai",
"Chancharik Mitra",
"Brandon Huang",
"Gautam Rajendrakumar Gare",
"Zhiqiu Lin",
"Assaf Arbelle",
"Leonid Karlinsky",
"Rogerio Feris",
"Trevor Darrell",
"Deva Ramanan",
"Roei Herzig"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-07-02T00:00:00 | https://arxiv.org/abs/2507.01368 | https://arxiv.org/pdf/2507.01368v1 | 2507.01368 | 10.48550/arXiv.2507.01368 | 2 | 0 | false | null | arXiv.org | 0.2154 |
cd095d3ab3942adbfdc57a73f504426c4ea61b26a96ac5972080c23a74fe32e5 | [
"arxiv",
"semantic_scholar"
] | Residual Reward Models for Preference-based Reinforcement Learning | Preference-based Reinforcement Learning (PbRL) provides a way to learn high-performance policies in environments where the reward signal is hard to specify, avoiding heuristic and time-consuming reward design. However, PbRL can suffer from slow convergence speed since it requires training in a reward model. Prior work ... | [
"Chenyang Cao",
"Miguel Rogel-García",
"Mohamed Nabail",
"Xueqian Wang",
"Nicholas Rhinehart"
] | [
"cs.LG",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2025-07-01T00:00:00 | https://arxiv.org/abs/2507.00611 | https://arxiv.org/pdf/2507.00611v1 | 2507.00611 | 10.48550/arXiv.2507.00611 | 2 | 1 | false | null | arXiv.org | 0.2143 |
9b388b7ecad4064a32451de556f4482595179c1871ab5120dcd76c66dcb51cea | [
"arxiv",
"semantic_scholar"
] | AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning | Rule-based rewards offer a promising strategy for improving reinforcement learning from human feedback (RLHF), but current approaches often rely on manual rule engineering. We present AutoRule, a fully automated method for extracting rules from preference feedback and formulating them into rule-based rewards. AutoRule ... | [
"Tevin Wang",
"Chenyan Xiong"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-06-18T00:00:00 | https://arxiv.org/abs/2506.15651 | https://arxiv.org/pdf/2506.15651v1 | 2506.15651 | 10.48550/arXiv.2506.15651 | 9 | 2 | true | https://github.com/cxcscmu/AutoRule | arXiv.org | 0.3081 |
4645bdf06984e503f6d43074c19d8d3ac9dc59d6c1e0c26162c454d7aff85b06 | [
"arxiv",
"semantic_scholar"
] | Reward Models in Deep Reinforcement Learning: A Survey | In reinforcement learning (RL), agents continually interact with the environment and use the feedback to refine their behavior. To guide policy optimization, reward models are introduced as proxies of the desired objectives, such that when the agent maximizes the accumulated reward, it also fulfills the task designer's... | [
"Rui Yu",
"Shenghua Wan",
"Yucen Wang",
"Chen-Xiao Gao",
"Le Gan",
"Zongzhang Zhang",
"De-Chuan Zhan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-18T00:00:00 | https://arxiv.org/abs/2506.15421 | https://arxiv.org/pdf/2506.15421v1 | 2506.15421 | 10.48550/arXiv.2506.15421 | 28 | 1 | false | null | International Joint Conference on Artificial Intelligence | 0.3656 |
58d0bedfbae10d42cfd2510ce7effd7659cf04ad02b8ac104fa07687495c49ab | [
"arxiv",
"semantic_scholar"
] | A General Framework for Off-Policy Learning with Partially-Observed Reward | Off-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only historical interaction data collected under previously developed policies. Unfortunately, when rewards are only partially observed, the effectiveness of OPL degrades severely. Well-know... | [
"Rikiya Takehi",
"Masahiro Asami",
"Kosuke Kawakami",
"Yuta Saito"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-06-17T00:00:00 | https://arxiv.org/abs/2506.14439 | https://arxiv.org/pdf/2506.14439v1 | 2506.14439 | 10.48550/arXiv.2506.14439 | 1 | 0 | false | null | International Conference on Learning Representations | 0.1982 |
da2eee8ece386cc92ee841673ef9c046a8be31977e6f7e91d756ac9356c0df81 | [
"arxiv",
"semantic_scholar"
] | TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization | Recent advancements in reinforcement learning from human feedback have shown that utilizing fine-grained token-level reward models can substantially enhance the performance of Proximal Policy Optimization (PPO) in aligning large language models. However, it is challenging to leverage such token-level reward as guidance... | [
"Mingkang Zhu",
"Xi Chen",
"Zhongdao Wang",
"Bei Yu",
"Hengshuang Zhao",
"Jiaya Jia"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-06-17T00:00:00 | https://arxiv.org/abs/2506.14574 | https://arxiv.org/pdf/2506.14574v1 | 2506.14574 | 10.48550/arXiv.2506.14574 | 6 | 0 | true | https://github.com/dvlab-research/TGDPO | International Conference on Machine Learning | 0.3064 |
155b7e74e3282ba27ef05598bed14b68c7c23386d62407e3e91574eed0ecca66 | [
"arxiv",
"semantic_scholar"
] | Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization | Numerous heuristics and advanced approaches have been proposed for exploration in different settings for deep reinforcement learning. Noise-based exploration generally fares well with dense-shaped rewards and bonus-based exploration with sparse rewards. However, these methods usually require additional tuning to deal w... | [
"Sebastian Griesbach",
"Carlo D'Eramo"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-06-16T00:00:00 | https://arxiv.org/abs/2506.13345 | https://arxiv.org/pdf/2506.13345v2 | 2506.13345 | 10.48550/arXiv.2506.13345 | 3 | 1 | false | null | arXiv.org | 0.1971 |
3c6cdc1b7059053ba080edb0b2ad867579667988676be916a089956c890c7eba | [
"arxiv",
"semantic_scholar"
] | Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning | Preference-based Reinforcement Learning (PbRL) entails a variety of approaches for aligning models with human intent to alleviate the burden of reward engineering. However, most previous PbRL work has not investigated the robustness to labeler errors, inevitable with labelers who are non-experts or operate under time c... | [
"Sara Rajaram",
"R. James Cotton",
"Fabian H. Sinz"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-06-14T00:00:00 | https://arxiv.org/abs/2506.12529 | https://arxiv.org/pdf/2506.12529v1 | 2506.12529 | 10.48550/arXiv.2506.12529 | 2 | 0 | false | null | arXiv.org | 0.1948 |
3a51464484b8fb38ab68447771f1f83aa81d46ba950875d5a2f98a51c4659d40 | [
"arxiv",
"semantic_scholar"
] | Multi-Task Reward Learning from Human Ratings | Reinforcement learning from human feedback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification... | [
"Mingkang Wu",
"Devin White",
"Evelyn Rose",
"Vernon Lawhern",
"Nicholas R Waytowich",
"Yongcan Cao"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-10T00:00:00 | https://arxiv.org/abs/2506.09183 | https://arxiv.org/pdf/2506.09183v2 | 2506.09183 | 10.48550/arXiv.2506.09183 | 1 | 1 | false | null | arXiv.org | 0.1902 |
e44c85e3e81318c8d3ca27c07416fc8688ff6c361ea1c45bc49bb145cbfcd92d | [
"arxiv",
"semantic_scholar"
] | Intra-Trajectory Consistency for Reward Modeling | Reward models are critical for improving large language models (LLMs), particularly in reinforcement learning from human feedback (RLHF) or inference-time verification. Current reward modeling typically relies on scores of overall responses to learn the outcome rewards for the responses. However, since the response-lev... | [
"Chaoyang Zhou",
"Shunyu Liu",
"Zengmao Wang",
"Di Wang",
"Rong-Cheng Tu",
"Bo Du",
"Dacheng Tao"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-10T00:00:00 | https://arxiv.org/abs/2506.09096 | https://arxiv.org/pdf/2506.09096v3 | 2506.09096 | 10.48550/arXiv.2506.09096 | 0 | 0 | true | https://github.com/chaoyang101/ICRM | arXiv.org | 0.294 |
acdb1c67eb8e501130bb9adc082d54b00cbdf3de0f74130edca567b6494a6eba | [
"arxiv",
"semantic_scholar"
] | GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO | The ability to train high-performing reward models with few-shot data is critical for enhancing the efficiency and scalability of Reinforcement Learning from Human Feedback (RLHF). We propose a data augmentation and expansion framework that enables generative reward models trained on small datasets to achieve comparabl... | [
"Yiyang Zhao",
"Huiyu Bai",
"Xuejiao Zhao"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-10T00:00:00 | https://arxiv.org/abs/2506.08965 | https://arxiv.org/pdf/2506.08965v1 | 2506.08965 | 10.48550/arXiv.2506.08965 | 0 | 0 | false | null | arXiv.org | 0.1902 |
e42d4253c2f889bbbc4d97782b8fd8f6078211cb41b1638e0d158f28d5dca451 | [
"arxiv",
"semantic_scholar"
] | Explicit Preference Optimization: No Need for an Implicit Reward Model | The generated responses of large language models (LLMs) are often fine-tuned to human preferences through a process called reinforcement learning from human feedback (RLHF). As RLHF relies on a challenging training sequence, whereby a separate reward model is independently learned and then later applied to LLM policy u... | [
"Xiangkun Hu",
"Lemin Kong",
"Tong He",
"David Wipf"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-06-09T00:00:00 | https://arxiv.org/abs/2506.07492 | https://arxiv.org/pdf/2506.07492v1 | 2506.07492 | 10.48550/arXiv.2506.07492 | 5 | 0 | false | null | International Conference on Machine Learning | 0.1945 |
2ad7e7671e1fe9c422089ebc327a17cf009e2ff72aed5940a6eb3417f3ce1d0a | [
"arxiv",
"semantic_scholar"
] | AMoPO: Adaptive Multi-objective Preference Optimization without Reward Models and Reference Models | Existing multi-objective preference alignment methods for large language models (LLMs) face limitations: (1) the inability to effectively balance various preference dimensions, and (2) reliance on auxiliary reward/reference models introduces computational complexity. To address these challenges, we propose Adaptive Mul... | [
"Qi Liu",
"Jingqing Ruan",
"Hao Li",
"Haodong Zhao",
"Desheng Wang",
"Jiansong Chen",
"Wan Guanglu",
"Xunliang Cai",
"Zhi Zheng",
"Tong Xu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-08T00:00:00 | https://arxiv.org/abs/2506.07165 | https://arxiv.org/pdf/2506.07165v1 | 2506.07165 | 10.48550/arXiv.2506.07165 | 8 | 1 | true | https://github.com/Javkonline/AMoPO | Annual Meeting of the Association for Computational Linguistics | 0.2904 |
6a3f5aac88fd55555fee8029ecbbe11f6e1c4bc935d4b9c267cff40932520e37 | [
"arxiv",
"semantic_scholar"
] | BadReward: Clean-Label Poisoning of Reward Models in Text-to-Image RLHF | Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning text-to-image (T2I) models with human preferences. However, RLHF's feedback mechanism also opens new pathways for adversaries. This paper demonstrates the feasibility of hijacking T2I models by poisoning a small fraction of preference data with n... | [
"Kaiwen Duan",
"Hongwei Yao",
"Yufei Chen",
"Ziyun Li",
"Tong Qiao",
"Zhan Qin",
"Cong Wang"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2025-06-03T00:00:00 | https://arxiv.org/abs/2506.03234 | https://arxiv.org/pdf/2506.03234v1 | 2506.03234 | 10.48550/arXiv.2506.03234 | 3 | 0 | false | null | arXiv.org | 0.1822 |
c67feb08b1efbdee3f5245fcd6a488fa38981befd75d780ac97b540ceb4e9200 | [
"arxiv",
"semantic_scholar"
] | RewardBench 2: Advancing Reward Model Evaluation | Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a training target for optimization across instruction following, reasoning, safety, and more domains. The community has begun establishing best practices for evaluating reward models, from ... | [
"Saumya Malik",
"Valentina Pyatkin",
"Sander Land",
"Jacob Morrison",
"Noah A. Smith",
"Hannaneh Hajishirzi",
"Nathan Lambert"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-06-02T00:00:00 | https://arxiv.org/abs/2506.01937 | https://arxiv.org/pdf/2506.01937v2 | 2506.01937 | 10.48550/arXiv.2506.01937 | 100 | 22 | false | null | arXiv.org | 0.6809 |
17a35f64c9d38f0ffae023cb4c2a6bcc39d7caec9dbfbca99de868440badd091 | [
"arxiv",
"semantic_scholar"
] | Accelerating RLHF Training with Reward Variance Increase | Reinforcement learning from human feedback (RLHF) is an essential technique for ensuring that large language models (LLMs) are aligned with human values and preferences during the post-training phase. As an effective RLHF approach, group relative policy optimization (GRPO) has demonstrated success in many LLM-based app... | [
"Zonglin Yang",
"Zhexuan Gu",
"Houduo Qi",
"Yancheng Yuan"
] | [
"cs.LG",
"cs.AI",
"math.OC"
] | [
"Computer Science",
"Mathematics"
] | 2025-05-29T00:00:00 | https://arxiv.org/abs/2505.23247 | https://arxiv.org/pdf/2505.23247v2 | 2505.23247 | 10.48550/arXiv.2505.23247 | 1 | 0 | false | null | arXiv.org | 0.1765 |
3ebf66805ee3bd1222f64bbb6b45f239de68a480cf0a1f63c41585b5d80316c9 | [
"arxiv",
"semantic_scholar"
] | Towards Reward Fairness in RLHF: From a Resource Allocation Perspective | Rewards serve as proxies for human preferences and play a crucial role in Reinforcement Learning from Human Feedback (RLHF). However, if these rewards are inherently imperfect, exhibiting various biases, they can adversely affect the alignment of large language models (LLMs). In this paper, we collectively define the v... | [
"Sheng Ouyang",
"Yulan Hu",
"Ge Chen",
"Qingyang Li",
"Fuzheng Zhang",
"Yong Liu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-29T00:00:00 | https://arxiv.org/abs/2505.23349 | https://arxiv.org/pdf/2505.23349v1 | 2505.23349 | 10.18653/v1/2025.acl-long.163 | 12 | 2 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2785 |
dcb5997c76565e63cb72d094b02260523faef7fded02f5fc0df61da80e0f17d6 | [
"arxiv",
"semantic_scholar"
] | Learning a Pessimistic Reward Model in RLHF | This work proposes `PET', a novel pessimistic reward fine-tuning method, to learn a pessimistic reward model robust against reward hacking in offline reinforcement learning from human feedback (RLHF). Traditional reward modeling techniques in RLHF train an imperfect reward model, on which a KL regularization plays a pi... | [
"Yinglun Xu",
"Hangoo Kang",
"Tarun Suresh",
"Yuxuan Wan",
"Gagandeep Singh"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-05-26T00:00:00 | https://arxiv.org/abs/2505.20556 | https://arxiv.org/pdf/2505.20556v1 | 2505.20556 | 10.48550/arXiv.2505.20556 | 5 | 1 | false | null | arXiv.org | 0.1945 |
9080ca3d8309373118e20454a75d9c0b7497a98e4c553d80e2a8551c00b7a024 | [
"arxiv",
"semantic_scholar"
] | Understanding the Performance Gap in Preference Learning: A Dichotomy of RLHF and DPO | We present a fine-grained theoretical analysis of the performance gap between two-stage reinforcement learning from human feedback~(RLHF) and direct preference optimization~(DPO). Our study decomposes this gap into two sources: the explicit representation gap under exact optimization and the implicit representation gap... | [
"Ruizhe Shi",
"Minhak Song",
"Runlong Zhou",
"Zihan Zhang",
"Maryam Fazel",
"Simon S. Du"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-05-26T00:00:00 | https://arxiv.org/abs/2505.19770 | https://arxiv.org/pdf/2505.19770v5 | 2505.19770 | 10.48550/arXiv.2505.19770 | 10 | 1 | false | null | arXiv.org | 0.2603 |
61e2106a41124afb7efd442f3ceb89f3cead9caff1455a8988827998e3f53b70 | [
"arxiv",
"semantic_scholar"
] | MOSLIM:Align with diverse preferences in prompts through reward classification | The multi-objective alignment of Large Language Models (LLMs) is essential for ensuring foundational models conform to diverse human preferences. Current research in this field typically involves either multiple policies or multiple reward models customized for various preferences, or the need to train a preference-spe... | [
"Yu Zhang",
"Wanli Jiang",
"Zhengyu Yang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-24T00:00:00 | https://arxiv.org/abs/2505.20336 | https://arxiv.org/pdf/2505.20336v1 | 2505.20336 | 10.48550/arXiv.2505.20336 | 3 | 0 | false | null | arXiv.org | 0.1707 |
d18be7733c0fd29534dea4d41883e67e738dd9f9de135f39b096bbeb3954deb1 | [
"arxiv",
"semantic_scholar"
] | Reward Model Overoptimisation in Iterated RLHF | Reinforcement learning from human feedback (RLHF) is a widely used method for aligning large language models with human preferences. However, RLHF often suffers from reward model overoptimisation, in which models overfit to the reward function, resulting in non-generalisable policies that exploit the idiosyncrasies and... | [
"Lorenz Wolf",
"Robert Kirk",
"Mirco Musolesi"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-05-23T00:00:00 | https://arxiv.org/abs/2505.18126 | https://arxiv.org/pdf/2505.18126v2 | 2505.18126 | 10.48550/arXiv.2505.18126 | 5 | 1 | false | null | arXiv.org | 0.1945 |
d1f29db2306123334fc5447c67bf4c3698cc491dfe17f5f30f05a2c17f5ec6ce | [
"arxiv",
"semantic_scholar"
] | Think-RM: Enabling Long-Horizon Reasoning in Generative Reward Models | Reinforcement learning from human feedback (RLHF) has become a powerful post-training paradigm for aligning large language models with human preferences. A core challenge in RLHF is constructing accurate reward signals, where the conventional Bradley-Terry reward models (BT RMs) often suffer from sensitivity to data si... | [
"Ilgee Hong",
"Changlong Yu",
"Liang Qiu",
"Weixiang Yan",
"Zhenghao Xu",
"Haoming Jiang",
"Qingru Zhang",
"Qin Lu",
"Xin Liu",
"Chao Zhang",
"Tuo Zhao"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-05-22T00:00:00 | https://arxiv.org/abs/2505.16265 | https://arxiv.org/pdf/2505.16265v1 | 2505.16265 | 10.48550/arXiv.2505.16265 | 11 | 3 | false | null | arXiv.org | 0.301 |
947b81b4f5e9316484a0d8d5370949384e4fd7316f009f451303b29bc2147a7d | [
"arxiv",
"semantic_scholar"
] | Reward Reasoning Model | Reward models play a critical role in guiding large language models toward outputs that align with human expectations. However, an open challenge remains in effectively utilizing test-time compute to enhance reward model performance. In this work, we introduce Reward Reasoning Models (RRMs), which are specifically desi... | [
"Jiaxin Guo",
"Zewen Chi",
"Li Dong",
"Qingxiu Dong",
"Xun Wu",
"Shaohan Huang",
"Furu Wei"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-20T00:00:00 | https://arxiv.org/abs/2505.14674 | https://arxiv.org/pdf/2505.14674v1 | 2505.14674 | 10.48550/arXiv.2505.14674 | 35 | 4 | false | null | arXiv.org | 0.3891 |
319da1303360bd98715a2d3605071061a190aa7792a308950cdf0253f15631e0 | [
"arxiv",
"semantic_scholar"
] | Bias Fitting to Mitigate Length Bias of Reward Model in RLHF | Reinforcement Learning from Human Feedback relies on reward models to align large language models with human preferences. However, RLHF often suffers from reward hacking, wherein policy learning exploits flaws in the trained reward model to maximize reward scores without genuinely aligning with human preferences. A sig... | [
"Kangwen Zhao",
"Jianfeng Cai",
"Jinhua Zhu",
"Ruopei Sun",
"Dongyun Xue",
"Wengang Zhou",
"Li Li",
"Houqiang Li"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-19T00:00:00 | https://arxiv.org/abs/2505.12843 | https://arxiv.org/pdf/2505.12843v1 | 2505.12843 | 10.48550/arXiv.2505.12843 | 7 | 0 | false | null | arXiv.org | 0.2258 |
ec39b36546472882af42521050f29e6acbff0edde3de1206625b34c2044c7ed6 | [
"arxiv",
"semantic_scholar"
] | Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization | Reward models (RMs) play a crucial role in reinforcement learning from human feedback (RLHF), aligning model behavior with human preferences. However, existing benchmarks for reward models show a weak correlation with the performance of optimized policies, suggesting that they fail to accurately assess the true capabil... | [
"Sunghwan Kim",
"Dongjin Kang",
"Taeyoon Kwon",
"Hyungjoo Chae",
"Dongha Lee",
"Jinyoung Yeo"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-05-19T00:00:00 | https://arxiv.org/abs/2505.12763 | https://arxiv.org/pdf/2505.12763v1 | 2505.12763 | 10.48550/arXiv.2505.12763 | 7 | 1 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.2258 |
bc8c6db98eb94e802239b6901ef44a6fd6be8d3b5500d0260a84699df6e623f7 | [
"arxiv",
"semantic_scholar"
] | Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning | As generative agents become increasingly capable, alignment of their behavior with complex human values remains a fundamental challenge. Existing approaches often simplify human intent through reduction to a scalar reward, overlooking the multi-faceted nature of human feedback. In this work, we introduce a theoretical ... | [
"Kalyan Cherukuri",
"Aarav Lala"
] | [
"cs.LG",
"cs.AI",
"cs.CG"
] | [
"Computer Science"
] | 2025-05-17T00:00:00 | https://arxiv.org/abs/2505.11864 | https://arxiv.org/pdf/2505.11864v3 | 2505.11864 | 10.48550/arXiv.2505.11864 | 1 | 0 | false | null | arXiv.org | 0.1627 |
f52fd260499c3a19e4e32b879fbf872f179a1a334e51c318c2cfe207e013737e | [
"arxiv",
"semantic_scholar"
] | Detecting Prefix Bias in LLM-based Reward Models | Reinforcement Learning with Human Feedback (RLHF) has emerged as a key paradigm for task-specific fine-tuning of language models using human preference data. While numerous publicly available preference datasets provide pairwise comparisons of responses, the potential for biases in the resulting reward models remains u... | [
"Ashwin Kumar",
"Yuzi He",
"Aram H. Markosyan",
"Bobbie Chern",
"Imanol Arrieta-Ibarra"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-13T00:00:00 | https://arxiv.org/abs/2505.13487 | https://arxiv.org/pdf/2505.13487v2 | 2505.13487 | 10.1145/3715275.3732204 | 13 | 1 | true | null | Conference on Fairness, Accountability and Transparency | 0.2865 |
9177fd7c623e81899daaa21d3a2583d6809ec941b4f845c3bb7a0433b993d6d4 | [
"arxiv",
"semantic_scholar"
] | On the Robustness of Reward Models for Language Model Alignment | The Bradley-Terry (BT) model is widely practiced in reward modeling for reinforcement learning with human feedback (RLHF). Despite its effectiveness, reward models (RMs) trained with BT model loss are prone to over-optimization, losing generalizability to unseen input distributions. In this paper, we study the cause of... | [
"Jiwoo Hong",
"Noah Lee",
"Eunki Kim",
"Guijin Son",
"Woojin Chung",
"Aman Gupta",
"Shao Tang",
"James Thorne"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-12T00:00:00 | https://arxiv.org/abs/2505.07271 | https://arxiv.org/pdf/2505.07271v1 | 2505.07271 | 10.48550/arXiv.2505.07271 | 11 | 0 | true | https://github.com/LinkedIn-XFACT/RM-Robustness | International Conference on Machine Learning | 0.2698 |
c65e399c70be1f592947a35b249bf97aa5a7c65dea4131bd6bad673fe27f41db | [
"arxiv",
"semantic_scholar"
] | Learning Guarantee of Reward Modeling Using Deep Neural Networks | In this work, we study the learning theory of reward modeling with pairwise comparison data using deep neural networks. We establish a novel non-asymptotic regret bound for deep reward estimators in a non-parametric setting, which depends explicitly on the network architecture. Furthermore, to underscore the critical i... | [
"Yuanhang Luo",
"Yeheng Ge",
"Ruijian Han",
"Guohao Shen"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2025-05-10T00:00:00 | https://arxiv.org/abs/2505.06601 | https://arxiv.org/pdf/2505.06601v1 | 2505.06601 | 10.1145/3770854.3780316 | 3 | 0 | false | null | Knowledge Discovery and Data Mining | 0.1547 |
666b2c5f57a8c9ecbb18268f706a919ebc5f6e81a213b549b8ea8eb6e6e753ae | [
"arxiv",
"semantic_scholar"
] | DMRL: Data- and Model-aware Reward Learning for Data Extraction | Large language models (LLMs) are inherently vulnerable to unintended privacy breaches. Consequently, systematic red-teaming research is essential for developing robust defense mechanisms. However, current data extraction methods suffer from several limitations: (1) rely on dataset duplicates (addressable via deduplicat... | [
"Zhiqiang Wang",
"Ruoxi Cheng"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2025-05-07T00:00:00 | https://arxiv.org/abs/2505.06284 | https://arxiv.org/pdf/2505.06284v1 | 2505.06284 | 10.48550/arXiv.2505.06284 | 0 | 0 | false | null | arXiv.org | 0.1513 |
6e030a5fd3aa9ced9849c76019f2493a53b3f913b3bd6566a1aa72a632a75923 | [
"arxiv",
"semantic_scholar"
] | Policy-labeled Preference Learning: Is Preference Enough for RLHF? | To design rewards that align with human goals, Reinforcement Learning from Human Feedback (RLHF) has emerged as a prominent technique for learning reward functions from human preferences and optimizing policies via reinforcement learning algorithms. However, existing RLHF methods often misinterpret trajectories as bein... | [
"Taehyun Cho",
"Seokhun Ju",
"Seungyub Han",
"Dohyeong Kim",
"Kyungjae Lee",
"Jungwoo Lee"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-06T00:00:00 | https://arxiv.org/abs/2505.06273 | https://arxiv.org/pdf/2505.06273v2 | 2505.06273 | 10.48550/arXiv.2505.06273 | 3 | 0 | false | null | International Conference on Machine Learning | 0.1505 |
3c658dc8a481b46e4da551de8411b504d0dc36323c24d2c2612fd751b59162c0 | [
"arxiv",
"semantic_scholar"
] | Sailing by the Stars: A Survey on Reward Models and Learning Strategies for Learning from Rewards | Recent developments in Large Language Models (LLMs) have shifted from pre-training scaling to post-training and test-time scaling. Across these developments, a key unified paradigm has arisen: Learning from Rewards, where reward signals act as the guiding stars to steer LLM behavior. It has underpinned a wide range of ... | [
"Xiaobao Wu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-05-05T00:00:00 | https://arxiv.org/abs/2505.02686 | https://arxiv.org/pdf/2505.02686v2 | 2505.02686 | null | 7 | 0 | true | https://github.com/bobxwu/learning-from-rewards-llm-papers | null | 0.2258 |
647e2b573a55bc08bcb6e55eef68b9e7c8d35f3ecc19b634a916b994768c66fd | [
"arxiv",
"semantic_scholar"
] | R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning | Multimodal Reward Models (MRMs) play a crucial role in enhancing the performance of Multimodal Large Language Models (MLLMs). While recent advancements have primarily focused on improving the model structure and training data of MRMs, there has been limited exploration into the effectiveness of long-term reasoning capa... | [
"Yi-Fan Zhang",
"Xingyu Lu",
"Xiao Hu",
"Chaoyou Fu",
"Bin Wen",
"Tianke Zhang",
"Changyi Liu",
"Kaiyu Jiang",
"Kaibing Chen",
"Kaiyu Tang",
"Haojie Ding",
"Jiankang Chen",
"Fan Yang",
"Zhang Zhang",
"Tingting Gao",
"Liang Wang"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2025-05-05T00:00:00 | https://arxiv.org/abs/2505.02835 | https://arxiv.org/pdf/2505.02835v2 | 2505.02835 | 10.48550/arXiv.2505.02835 | 60 | 5 | true | https://github.com/yfzhang114/r1_reward | arXiv.org | 0.4463 |
4112a6e5900033e5067279fa930bb6d6dc68b3b1fd85be38c58b82f149846151 | [
"arxiv",
"semantic_scholar"
] | RM-R1: Reward Modeling as Reasoning | Reward modeling is essential for aligning large language models with human preferences through reinforcement learning. To provide accurate reward signals, a reward model (RM) should stimulate deep thinking and conduct interpretable reasoning before assigning a score or a judgment. Inspired by recent advances of long ch... | [
"Xiusi Chen",
"Gaotang Li",
"Ziqi Wang",
"Bowen Jin",
"Cheng Qian",
"Yu Wang",
"Hongru Wang",
"Yu Zhang",
"Denghui Zhang",
"Tong Zhang",
"Hanghang Tong",
"Heng Ji"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-05T00:00:00 | https://arxiv.org/abs/2505.02387 | https://arxiv.org/pdf/2505.02387v4 | 2505.02387 | 10.48550/arXiv.2505.02387 | 129 | 24 | false | null | arXiv.org | 0.699 |
94e00a7af75a5865ec9b5cd109304dbad8efd77b2fd7718dd8e1d48ccfb13ea7 | [
"arxiv",
"semantic_scholar"
] | CaRL: Learning Scalable Planning Policies with Simple Rewards | We investigate reinforcement learning (RL) for privileged planning in autonomous driving. State-of-the-art approaches for this task are rule-based, but these methods do not scale to the long tail. RL, on the other hand, is scalable and does not suffer from compounding errors like imitation learning. Contemporary RL app... | [
"Bernhard Jaeger",
"Daniel Dauner",
"Jens Beißwenger",
"Simon Gerstenecker",
"Kashyap Chitta",
"Andreas Geiger"
] | [
"cs.LG",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2025-04-24T00:00:00 | https://arxiv.org/abs/2504.17838 | https://arxiv.org/pdf/2504.17838v3 | 2504.17838 | 10.48550/arXiv.2504.17838 | 26 | 1 | false | null | arXiv.org | 0.3578 |
c276d6b2a94d6af40c70bf1aafdd1a083beb61d089c31c57794573fbdd8c0fc6 | [
"arxiv",
"semantic_scholar"
] | Learning Explainable Dense Reward Shapes via Bayesian Optimization | Current reinforcement learning from human feedback (RLHF) pipelines for large language model (LLM) alignment typically assign scalar rewards to sequences, using the final token as a surrogate indicator for the quality of the entire sequence. However, this leads to sparse feedback and suboptimal token-level credit assig... | [
"Ryan Koo",
"Ian Yang",
"Vipul Raheja",
"Mingyi Hong",
"Kwang-Sung Jun",
"Dongyeop Kang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-04-22T00:00:00 | https://arxiv.org/abs/2504.16272 | https://arxiv.org/pdf/2504.16272v1 | 2504.16272 | 10.48550/arXiv.2504.16272 | 2 | 0 | false | null | arXiv.org | 0.1341 |
be2bf58c15f704843c7932ba5702c4a6fc5c21dcc5ad589fa915e7a49ce2a53e | [
"arxiv",
"semantic_scholar"
] | LoRe: Personalizing LLMs via Low-Rank Reward Modeling | Personalizing large language models (LLMs) to accommodate diverse user preferences is essential for enhancing alignment and user satisfaction. Traditional reinforcement learning from human feedback (RLHF) approaches often rely on monolithic value representations, limiting their ability to adapt to individual preference... | [
"Avinandan Bose",
"Zhihan Xiong",
"Yuejie Chi",
"Simon Shaolei Du",
"Lin Xiao",
"Maryam Fazel"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-04-20T00:00:00 | https://arxiv.org/abs/2504.14439 | https://arxiv.org/pdf/2504.14439v1 | 2504.14439 | 10.48550/arXiv.2504.14439 | 24 | 3 | false | null | arXiv.org | 0.3495 |
3b218257623054ec67457c9ae710bc0e0449e57d1bc8a6b1c6176b3b2677f41c | [
"arxiv",
"semantic_scholar"
] | CHARM: Calibrating Reward Models With Chatbot Arena Scores | Reward models (RMs) play a crucial role in Reinforcement Learning from Human Feedback by serving as proxies for human preferences in aligning large language models. However, they suffer from various biases which could lead to reward hacking. In this paper, we identify a model preference bias in RMs, where they systemat... | [
"Xiao Zhu",
"Chenmien Tan",
"Pinzhen Chen",
"Rico Sennrich",
"Huiming Wang",
"Yanlin Zhang",
"Hanxu Hu"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-04-14T00:00:00 | https://arxiv.org/abs/2504.10045 | https://arxiv.org/pdf/2504.10045v2 | 2504.10045 | 10.48550/arXiv.2504.10045 | 4 | 0 | false | null | arXiv.org | 0.1747 |
23edfa7e4921483e120223b3fb315f50a55dec10bf5387ddd0180d9ff21be781 | [
"arxiv",
"semantic_scholar"
] | FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functions | Preference-based reinforcement learning (PbRL) is a suitable approach for style adaptation of pre-trained robotic behavior: adapting the robot's policy to follow human user preferences while still being able to perform the original task. However, collecting preferences for the adaptation process in robotics is often ch... | [
"Daniel Marta",
"Simon Holk",
"Miguel Vasco",
"Jens Lundell",
"Timon Homberger",
"Finn Busch",
"Olov Andersson",
"Danica Kragic",
"Iolanda Leite"
] | [
"cs.RO",
"cs.LG"
] | [
"Computer Science"
] | 2025-04-14T00:00:00 | https://arxiv.org/abs/2504.10002 | https://arxiv.org/pdf/2504.10002v1 | 2504.10002 | 10.1109/ICRA55743.2025.11127633 | 2 | 0 | false | null | IEEE International Conference on Robotics and Automation | 0.1249 |
7500e5c17e13d3a53adac64ffcdc54de62b1b93e57148722c19896df552ab46e | [
"arxiv",
"semantic_scholar"
] | Information-Theoretic Reward Decomposition for Generalizable RLHF | A generalizable reward model is crucial in Reinforcement Learning from Human Feedback (RLHF) as it enables correctly evaluating unseen prompt-response pairs. However, existing reward models lack this ability, as they are typically trained by increasing the reward gap between chosen and rejected responses, while overloo... | [
"Liyuan Mao",
"Haoran Xu",
"Amy Zhang",
"Weinan Zhang",
"Chenjia Bai"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-04-08T00:00:00 | https://arxiv.org/abs/2504.06020 | https://arxiv.org/pdf/2504.06020v2 | 2504.06020 | 10.48550/arXiv.2504.06020 | 4 | 1 | false | null | arXiv.org | 0.1747 |
c89b51520214f8e5f022b8b095e83c12f4b1dfd83457ea6e687d15bb62df98b5 | [
"arxiv",
"semantic_scholar"
] | Adversarial Training of Reward Models | Reward modeling has emerged as a promising approach for the scalable alignment of language models. However, contemporary reward models (RMs) often lack robustness, awarding high rewards to low-quality, out-of-distribution (OOD) samples. This can lead to reward hacking, where policies exploit unintended shortcuts to max... | [
"Alexander Bukharin",
"Haifeng Qian",
"Shengyang Sun",
"Adithya Renduchintala",
"Soumye Singhal",
"Zhilin Wang",
"Oleksii Kuchaiev",
"Olivier Delalleau",
"Tuo Zhao"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-04-08T00:00:00 | https://arxiv.org/abs/2504.06141 | https://arxiv.org/pdf/2504.06141v2 | 2504.06141 | 10.48550/arXiv.2504.06141 | 13 | 0 | false | null | arXiv.org | 0.2865 |
b5f392da3195630890228649bb006c9b911f99958a5359c4217b7a6b7c7f954c | [
"arxiv",
"semantic_scholar"
] | A Unified Pairwise Framework for RLHF: Bridging Generative Reward Modeling and Policy Optimization | Reinforcement Learning from Human Feedback (RLHF) has emerged as a important paradigm for aligning large language models (LLMs) with human preferences during post-training. This framework typically involves two stages: first, training a reward model on human preference data, followed by optimizing the language model us... | [
"Wenyuan Xu",
"Xiaochen Zuo",
"Chao Xin",
"Yu Yue",
"Lin Yan",
"Yonghui Wu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-04-07T00:00:00 | https://arxiv.org/abs/2504.04950 | https://arxiv.org/pdf/2504.04950v1 | 2504.04950 | 10.48550/arXiv.2504.04950 | 20 | 1 | false | null | arXiv.org | 0.3306 |
9b31d18a84bc4511fa9b579531827f8d4a998552f229b4fac0cf4b84441a86ee | [
"arxiv",
"semantic_scholar"
] | Reward Generation via Large Vision-Language Model in Offline Reinforcement Learning | In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward signals for offline dataset requires significant human effort and domain expertise. Reinforcement learnin... | [
"Younghwan Lee",
"Tung M. Luu",
"Donghoon Lee",
"Chang D. Yoo"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-04-03T00:00:00 | https://arxiv.org/abs/2504.08772 | https://arxiv.org/pdf/2504.08772v1 | 2504.08772 | 10.1109/ICASSP49660.2025.10889042 | 3 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.1505 |
45d08ff3375d1b91919777a58de6d5577947e0c1a3c487f43aeb2e460edc838c | [
"arxiv",
"semantic_scholar"
] | Probabilistic Uncertain Reward Model | Reinforcement learning from human feedback (RLHF) is a critical technique for training large language models. However, conventional reward models based on the Bradley-Terry model (BTRM) often suffer from overconfidence when faced with inconsistent labels or out-of-distribution samples, leading to reward hacking, where ... | [
"Wangtao Sun",
"Xiang Cheng",
"Xing Yu",
"Haotian Xu",
"Zhao Yang",
"Shizhu He",
"Jun Zhao",
"Kang Liu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-03-28T00:00:00 | https://arxiv.org/abs/2503.22480 | https://arxiv.org/pdf/2503.22480v6 | 2503.22480 | 10.48550/arXiv.2503.22480 | 1 | 0 | false | null | arXiv.org | 0.1054 |
44ad06fffad9ac7d2af1123140133961da71de87819ad8d2dfde121c6d78f789 | [
"arxiv",
"semantic_scholar"
] | Reward Design for Reinforcement Learning Agents | Reward functions are central in reinforcement learning (RL), guiding agents towards optimal decision-making. The complexity of RL tasks requires meticulously designed reward functions that effectively drive learning while avoiding unintended consequences. Effective reward design aims to provide signals that accelerate ... | [
"Rati Devidze"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-03-27T00:00:00 | https://arxiv.org/abs/2503.21949 | https://arxiv.org/pdf/2503.21949v1 | 2503.21949 | 10.48550/arXiv.2503.21949 | 4 | 0 | false | null | arXiv.org | 0.1747 |
d971ae1199ec2e89cd83e2da45084a13c1f73d0ec8fd59b4f47d7cc050bd46a7 | [
"arxiv",
"semantic_scholar"
] | ViLBench: A Suite for Vision-Language Process Reward Modeling | Process-supervised reward models serve as a fine-grained function that provides detailed step-wise feedback to model responses, facilitating effective selection of reasoning trajectories for complex tasks. Despite its advantages, evaluation on PRMs remains less explored, especially in the multimodal domain. To address ... | [
"Haoqin Tu",
"Weitao Feng",
"Hardy Chen",
"Hui Liu",
"Xianfeng Tang",
"Cihang Xie"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2025-03-26T00:00:00 | https://arxiv.org/abs/2503.20271 | https://arxiv.org/pdf/2503.20271v1 | 2503.20271 | 10.48550/arXiv.2503.20271 | 19 | 2 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.3253 |
a80334dc8886133768d587f17fed54544c0b28e9624004e414ee7182900f7e4f | [
"arxiv",
"semantic_scholar"
] | Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization | Reinforcement learning from human feedback (RLHF) is an effective method for aligning large language models (LLMs) with human values. However, reward over-optimization remains an open challenge leading to discrepancies between the performance of LLMs under the reward model and the true human objectives. A primary contr... | [
"Juntao Dai",
"Taiye Chen",
"Yaodong Yang",
"Qian Zheng",
"Gang Pan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-23T00:00:00 | https://arxiv.org/abs/2503.18130 | https://arxiv.org/pdf/2503.18130v1 | 2503.18130 | 10.48550/arXiv.2503.18130 | 10 | 1 | false | null | International Conference on Learning Representations | 0.2603 |
ccda508663d7898f7a49fde81ccd7f3b2217d75195d5ac3f34cba0f1a5c3d632 | [
"arxiv",
"semantic_scholar"
] | Capturing Individual Human Preferences with Reward Features | Reinforcement learning from human feedback usually models preferences using a reward function that does not distinguish between people. We argue that this is unlikely to be a good design choice in contexts with high potential for disagreement, like in the training of large language models. We formalise and analyse the ... | [
"André Barreto",
"Vincent Dumoulin",
"Yiran Mao",
"Mark Rowland",
"Nicolas Perez-Nieves",
"Bobak Shahriari",
"Yann Dauphin",
"Doina Precup",
"Hugo Larochelle"
] | [
"cs.AI",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-03-21T00:00:00 | https://arxiv.org/abs/2503.17338 | https://arxiv.org/pdf/2503.17338v2 | 2503.17338 | 10.48550/arXiv.2503.17338 | 11 | 1 | false | null | arXiv.org | 0.2698 |
f0b43246e955aca73fd63ed015c14d8290c37be52d543ece2081e812b5c92c8d | [
"arxiv",
"semantic_scholar"
] | Reward Redistribution via Gaussian Process Likelihood Estimation | In many practical reinforcement learning tasks, feedback is only provided at the end of a long horizon, leading to sparse and delayed rewards. Existing reward redistribution methods typically assume that per-step rewards are independent, thus overlooking interdependencies among state-action pairs. In this paper, we pro... | [
"Minheng Xiao",
"Xian Yu"
] | [
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2025-03-20T00:00:00 | https://arxiv.org/abs/2503.17409 | https://arxiv.org/pdf/2503.17409v2 | 2503.17409 | null | 0 | 0 | false | null | null | 0.0612 |
87061b5f8f9c20d43470a9a2b6a78f22ac05261df71bdf913d95e5feb9b5a99c | [
"arxiv",
"semantic_scholar"
] | What Makes a Reward Model a Good Teacher? An Optimization Perspective | The success of Reinforcement Learning from Human Feedback (RLHF) critically depends on the quality of the reward model. However, while this quality is primarily evaluated through accuracy, it remains unclear whether accuracy fully captures what makes a reward model an effective teacher. We address this question from an... | [
"Noam Razin",
"Zixuan Wang",
"Hubert Strauss",
"Stanley Wei",
"Jason D. Lee",
"Sanjeev Arora"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-03-19T00:00:00 | https://arxiv.org/abs/2503.15477 | https://arxiv.org/pdf/2503.15477v4 | 2503.15477 | 10.48550/arXiv.2503.15477 | 64 | 8 | true | https://github.com/princeton-pli/what-makes-good-rm | arXiv.org | 0.4771 |
ee30f7d9c4c841ffd3657072f5d8157dc8cdb493dd373ea97b4ce6f858e2b3b2 | [
"arxiv",
"semantic_scholar"
] | Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes | In this paper, we propose a new solution to reward adaptation (RA) in reinforcement learning, where the agent adapts to a target reward function based on one or more existing source behaviors learned a priori under the same domain dynamics but different reward functions. While learning the target behavior from scratch ... | [
"Kevin Vora",
"Yu Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-17T00:00:00 | https://arxiv.org/abs/2503.13414 | https://arxiv.org/pdf/2503.13414v3 | 2503.13414 | null | 1 | 0 | false | null | null | 0.0753 |
548ee58eb4a0c184cd4d2255620a3b14d571d5f1736dc1810201e41ba1a26995 | [
"arxiv",
"semantic_scholar"
] | From Demonstrations to Rewards: Alignment Without Explicit Human Preferences | One of the challenges of aligning large models with human preferences lies in both the data requirements and the technical complexities of current approaches. Predominant methods, such as RLHF, involve multiple steps, each demanding distinct types of data, including demonstration data and preference data. In RLHF, huma... | [
"Siliang Zeng",
"Yao Liu",
"Huzefa Rangwala",
"George Karypis",
"Mingyi Hong",
"Rasool Fakoor"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-15T00:00:00 | https://arxiv.org/abs/2503.13538 | https://arxiv.org/pdf/2503.13538v1 | 2503.13538 | 10.48550/arXiv.2503.13538 | 5 | 0 | false | null | arXiv.org | 0.1945 |
859549dd753d63b73b5a5b798bfe0562a21a2e9b81f54f431907168d25782e88 | [
"arxiv",
"semantic_scholar"
] | Right Reward Right Time for Federated Learning | Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data availability) can permanently impair the performance of the global model owned by the cloud server. However, existing incentive mechanisms typically assume temporal homoge... | [
"Thanh Linh Nguyen",
"Dinh Thai Hoang",
"Diep N. Nguyen",
"Quoc-Viet Pham"
] | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.GT"
] | [
"Computer Science"
] | 2025-03-10T00:00:00 | https://arxiv.org/abs/2503.07869 | https://arxiv.org/pdf/2503.07869v3 | 2503.07869 | 10.48550/arXiv.2503.07869 | 2 | 0 | false | null | arXiv.org | 0.1193 |
ecf9b9cdee2c1b17d877580efd168cd9dc614f7a372387773a7a071b608f8f5b | [
"arxiv",
"semantic_scholar"
] | Language Model Personalization via Reward Factorization | Modern large language models (LLMs) are optimized for human-aligned responses using Reinforcement Learning from Human Feedback (RLHF). However, existing RLHF approaches assume a universal preference model and fail to account for individual user preferences, limiting their effectiveness in personalized applications. We ... | [
"Idan Shenfeld",
"Felix Faltings",
"Pulkit Agrawal",
"Aldo Pacchiano"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-03-08T00:00:00 | https://arxiv.org/abs/2503.06358 | https://arxiv.org/pdf/2503.06358v1 | 2503.06358 | 10.48550/arXiv.2503.06358 | 23 | 4 | false | null | arXiv.org | 0.3495 |
91da2ec8eff57c25f9754497ab772339ab0dab5858165d4df843389d745955ea | [
"arxiv",
"semantic_scholar"
] | Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment | Direct Preference Optimization (DPO) has become a prominent method for aligning Large Language Models (LLMs) with human preferences. While DPO has enabled significant progress in aligning English LLMs, multilingual preference alignment is hampered by data scarcity. To address this, we propose a novel approach that $\te... | [
"Wen Yang",
"Junhong Wu",
"Chen Wang",
"Chengqing Zong",
"Jiajun Zhang"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-06T00:00:00 | https://arxiv.org/abs/2503.04647 | https://arxiv.org/pdf/2503.04647v2 | 2503.04647 | 10.48550/arXiv.2503.04647 | 6 | 0 | true | https://github.com/ZNLP/Implicit-Cross-Lingual-Rewarding | Annual Meeting of the Association for Computational Linguistics | 0.2113 |
04b8859e44d134c56a5b30b9c2a59ea8c10f08797bbde2cd069c5b2210d130e9 | [
"arxiv",
"semantic_scholar"
] | Diffusion Classifier-Driven Reward for Offline Preference-based Reinforcement Learning | Offline preference-based reinforcement learning (PbRL) mitigates the need for reward definition, aligning with human preferences via preference-driven reward feedback without interacting with the environment. However, trajectory-wise preference labels are difficult to meet the precise learning of step-wise reward, ther... | [
"Teng Pang",
"Bingzheng Wang",
"Guoqiang Wu",
"Yilong Yin"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-03-03T00:00:00 | https://arxiv.org/abs/2503.01143 | https://arxiv.org/pdf/2503.01143v3 | 2503.01143 | null | 0 | 0 | false | null | null | 0.0489 |
364037cda029563f86122dc266f4dcdb67e2adb4c8b1b9313200ef0c8205cd60 | [
"arxiv",
"semantic_scholar"
] | Sentence-level Reward Model can Generalize Better for Aligning LLM from Human Preference | Learning reward models from human preference datasets and subsequently optimizing language models via reinforcement learning has emerged as a fundamental paradigm for aligning LLMs with human preferences. The performance of the reward model plays a crucial role in the effectiveness of alignment. Previous reward models ... | [
"Wenjie Qiu",
"Yi-Chen Li",
"Xuqin Zhang",
"Tianyi Zhang",
"Yihang Zhang",
"Zongzhang Zhang",
"Yang Yu"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-01T00:00:00 | https://arxiv.org/abs/2503.04793 | https://arxiv.org/pdf/2503.04793v4 | 2503.04793 | 10.48550/arXiv.2503.04793 | 5 | 0 | false | null | arXiv.org | 0.1945 |
7a3f222ec9f4669af8212aee7125b5a05f40b366a397719706fb868c9b729e9c | [
"arxiv",
"semantic_scholar"
] | Reward Learning from Multiple Feedback Types | Learning rewards from preference feedback has become an important tool in the alignment of agentic models. Preference-based feedback, often implemented as a binary comparison between multiple completions, is an established method to acquire large-scale human feedback. However, human feedback in other contexts is often ... | [
"Yannick Metz",
"András Geiszl",
"Raphaël Baur",
"Mennatallah El-Assady"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-28T00:00:00 | https://arxiv.org/abs/2502.21038 | https://arxiv.org/pdf/2502.21038v1 | 2502.21038 | 10.48550/arXiv.2502.21038 | 7 | 0 | false | null | International Conference on Learning Representations | 0.2258 |
2db0ec7c2ab39bd180362a9a7937a17b0466d559caa39240ab823ef2d13dca27 | [
"arxiv",
"semantic_scholar"
] | Reward Shaping to Mitigate Reward Hacking in RLHF | Reinforcement Learning from Human Feedback (RLHF) is essential for aligning large language models (LLMs) with human values. However, RLHF is susceptible to \emph{reward hacking}, where the agent exploits flaws in the reward function rather than learning the intended behavior, thus degrading alignment. Although reward s... | [
"Jiayi Fu",
"Xuandong Zhao",
"Chengyuan Yao",
"Heng Wang",
"Qi Han",
"Yanghua Xiao"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.18770 | https://arxiv.org/pdf/2502.18770v5 | 2502.18770 | 10.48550/arXiv.2502.18770 | 78 | 7 | true | https://github.com/PorUna-byte/PAR | arXiv.org | 0.4744 |
503deaa5c3620362933f180c29e6c7911be811104e328a301dac5e3411863e3b | [
"arxiv",
"semantic_scholar"
] | Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective | Sample efficiency is critical for online Reinforcement Learning from Human Feedback (RLHF). While existing works investigate sample-efficient online exploration strategies, the potential of utilizing misspecified yet relevant reward models to accelerate learning remains underexplored. This paper studies how to transfer... | [
"Jiawei Huang",
"Bingcong Li",
"Christoph Dann",
"Niao He"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.19255 | https://arxiv.org/pdf/2502.19255v3 | 2502.19255 | 10.48550/arXiv.2502.19255 | 4 | 0 | false | null | International Conference on Machine Learning | 0.1747 |
44c02d9d0d7891d15271536a56ca7bde2f7aaeceedd5a8e104a9fdb348863b60 | [
"arxiv",
"semantic_scholar"
] | Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems | Reward models (RMs) are crucial for the training and inference-time scaling up of large language models (LLMs). However, existing reward models primarily focus on human preferences, neglecting verifiable correctness signals which have shown strong potential in training LLMs. In this paper, we propose agentic reward mod... | [
"Hao Peng",
"Yunjia Qi",
"Xiaozhi Wang",
"Zijun Yao",
"Bin Xu",
"Lei Hou",
"Juanzi Li"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.19328 | https://arxiv.org/pdf/2502.19328v1 | 2502.19328 | 10.48550/arXiv.2502.19328 | 49 | 4 | true | https://github.com/THU-KEG/Agentic-Reward-Modeling | Annual Meeting of the Association for Computational Linguistics | 0.4247 |
00e6bc7efdcdc603d11974e7e193e026eb6db7fcbdd67a71b436fa380922410b | [
"arxiv",
"semantic_scholar"
] | FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users | Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context capabilities of LLMs, we propose few-shot preference optimization (FSPO), an algorithm for LLM personalization that reframes reward modeling... | [
"Anikait Singh",
"Sheryl Hsu",
"Kyle Hsu",
"Eric Mitchell",
"Stefano Ermon",
"Tatsunori Hashimoto",
"Archit Sharma",
"Chelsea Finn"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.HC",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-02-26T00:00:00 | https://arxiv.org/abs/2502.19312 | https://arxiv.org/pdf/2502.19312v2 | 2502.19312 | null | 20 | 0 | false | null | null | 0.3306 |
e4cd76c36faf75ae1c8dc71ea566101507bf7aeb25b96998ccc282fd4695ac5d | [
"arxiv",
"semantic_scholar"
] | Larger or Smaller Reward Margins to Select Preferences for Alignment? | Preference learning is critical for aligning large language models (LLMs) with human values, with the quality of preference datasets playing a crucial role in this process. While existing metrics primarily assess data quality based on either explicit or implicit reward margins, they often provide contradictory evaluati... | [
"Kexin Huang",
"Junkang Wu",
"Ziqian Chen",
"Xue Wang",
"Jinyang Gao",
"Bolin Ding",
"Jiancan Wu",
"Xiangnan He",
"Xiang Wang"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-25T00:00:00 | https://arxiv.org/abs/2503.01864 | https://arxiv.org/pdf/2503.01864v1 | 2503.01864 | 10.48550/arXiv.2503.01864 | 9 | 2 | false | null | International Conference on Machine Learning | 0.25 |
d1045fea6a5cd25c5c9072c7e21ef8c500c2e70b31ed8596a6381768c709e211 | [
"arxiv",
"semantic_scholar"
] | Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data | Supervised fine-tuning (SFT) has become a crucial step for aligning pretrained large language models (LLMs) using supervised datasets of input-output pairs. However, despite being supervised, SFT is inherently limited by its generative training objective. To address its limitations, the existing common strategy is to f... | [
"Siqi Guo",
"Ilgee Hong",
"Vicente Balmaseda",
"Changlong Yu",
"Liang Qiu",
"Xin Liu",
"Haoming Jiang",
"Tuo Zhao",
"Tianbao Yang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-02-25T00:00:00 | https://arxiv.org/abs/2502.18679 | https://arxiv.org/pdf/2502.18679v3 | 2502.18679 | null | 1 | 0 | true | https://github.com/Optimization-AI/DFT | International Conference on Machine Learning | 0.108 |
546c7440afa4f5ecdd641a331f9c8167d0924faf13930c14421bd8a9e3c4c2bc | [
"arxiv",
"semantic_scholar"
] | Pretrain Value, Not Reward: Decoupled Value Policy Optimization | In this paper, we explore how directly pretraining a value model simplifies and stabilizes reinforcement learning from human feedback (RLHF). In reinforcement learning, value estimation is the key to policy optimization, distinct from reward supervision. The value function predicts the \emph{return-to-go} of a partial ... | [
"Chenghua Huang",
"Lu Wang",
"Fangkai Yang",
"Pu Zhao",
"Zhixu Li",
"Qingwei Lin",
"Dongmei Zhang",
"Saravan Rajmohan",
"Qi Zhang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-24T00:00:00 | https://arxiv.org/abs/2502.16944 | https://arxiv.org/pdf/2502.16944v2 | 2502.16944 | null | 4 | 0 | false | null | null | 0.1747 |
659b98252045b8b988bfeaa938420b40d200899bdfedf0ebcfdb670a7ff7088b | [
"arxiv",
"semantic_scholar"
] | RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment | Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning Large Language Models (LLMs) with human values. However, RLHF has been continuously challenged by its high complexity in implementation and computation consumption, specifically for online sampling-based methods like Proximal Policy Optimization ... | [
"Yuhao Du",
"Zhuo Li",
"Pengyu Cheng",
"Zhihong Chen",
"Yuejiao Xie",
"Xiang Wan",
"Anningzhe Gao"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-16T00:00:00 | https://arxiv.org/abs/2502.11026 | https://arxiv.org/pdf/2502.11026v3 | 2502.11026 | null | 14 | 0 | false | null | null | 0.294 |
a45c279e3a6d9db75715153b9d5987808b52c3d225ae14a2b4770ac3c2f66810 | [
"arxiv",
"semantic_scholar"
] | Process Reward Models for LLM Agents: Practical Framework and Directions | We introduce Agent Process Reward Models (AgentPRM), a simple and scalable framework for training LLM agents to continually improve through interactions. AgentPRM follows a lightweight actor-critic paradigm, using Monte Carlo rollouts to compute reward targets and optimize policies. It requires minimal modifications to... | [
"Sanjiban Choudhury"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-14T00:00:00 | https://arxiv.org/abs/2502.10325 | https://arxiv.org/pdf/2502.10325v1 | 2502.10325 | 10.48550/arXiv.2502.10325 | 66 | 4 | true | https://github.com/sanjibanc/agent_prm | arXiv.org | 0.4565 |
05be952385e975fc233797ffd481e9a4a7b665ef16ac4a543017ebe747792ef7 | [
"arxiv",
"semantic_scholar"
] | Provably Efficient Online RLHF with One-Pass Reward Modeling | Reinforcement Learning from Human Feedback (RLHF) has shown remarkable success in aligning Large Language Models (LLMs) with human preferences. Traditional RLHF methods rely on a fixed dataset, which often suffers from limited coverage. To this end, online RLHF has emerged as a promising direction, enabling iterative d... | [
"Long-Fei Li",
"Yu-Yang Qian",
"Peng Zhao",
"Zhi-Hua Zhou"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-02-11T00:00:00 | https://arxiv.org/abs/2502.07193 | https://arxiv.org/pdf/2502.07193v3 | 2502.07193 | null | 6 | 0 | false | null | null | 0.2113 |
84f4bc71d45cf2dbfb72b536d632b54bd111ecab05f02e2814d24a7def6f70b5 | [
"arxiv",
"semantic_scholar"
] | Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks | Collaborative learning enables multiple participants to learn a single global model by exchanging focused updates instead of sharing data. One of the core challenges in collaborative learning is ensuring that participants are rewarded fairly for their contributions, which entails two key sub-problems: contribution asse... | [
"Nurbek Tastan",
"Samuel Horvath",
"Karthik Nandakumar"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2025-02-07T00:00:00 | https://arxiv.org/abs/2502.04850 | https://arxiv.org/pdf/2502.04850v2 | 2502.04850 | 10.48550/arXiv.2502.04850 | 6 | 0 | false | null | International Conference on Machine Learning | 0.2113 |
4840c97328c5a423ef7e9fe324012617d39b1ed2a8a4d81e3d7cf6e7b73fdef0 | [
"arxiv",
"semantic_scholar"
] | PILAF: Optimal Human Preference Sampling for Reward Modeling | As large language models increasingly drive real-world applications, aligning them with human values becomes paramount. Reinforcement Learning from Human Feedback (RLHF) has emerged as a key technique, translating preference data into reward models when oracle human values remain inaccessible. In practice, RLHF mostly ... | [
"Yunzhen Feng",
"Ariel Kwiatkowski",
"Kunhao Zheng",
"Julia Kempe",
"Yaqi Duan"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-02-06T00:00:00 | https://arxiv.org/abs/2502.04270 | https://arxiv.org/pdf/2502.04270v1 | 2502.04270 | 10.48550/arXiv.2502.04270 | 18 | 1 | false | null | International Conference on Machine Learning | 0.3197 |
2066d042cc4261d2f1f01f49f10e698c8844f43abbe1725df679d6897784f218 | [
"arxiv",
"semantic_scholar"
] | Towards Cost-Effective Reward Guided Text Generation | Reward-guided text generation (RGTG) has emerged as a viable alternative to offline reinforcement learning from human feedback (RLHF). RGTG methods can align baseline language models to human preferences without further training like in standard RLHF methods. However, they rely on a reward model to score each candidate... | [
"Ahmad Rashid",
"Ruotian Wu",
"Rongqi Fan",
"Hongliang Li",
"Agustinus Kristiadi",
"Pascal Poupart"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-06T00:00:00 | https://arxiv.org/abs/2502.04517 | https://arxiv.org/pdf/2502.04517v2 | 2502.04517 | 10.48550/arXiv.2502.04517 | 6 | 0 | false | null | International Conference on Machine Learning | 0.2113 |
6200f20906672d86011fd07d8d65c6fef8d34f5d6f6164b0cdcbce53cb21863a | [
"arxiv",
"semantic_scholar"
] | PerPO: Perceptual Preference Optimization via Discriminative Rewarding | This paper presents Perceptual Preference Optimization (PerPO), a perception alignment method aimed at addressing the visual discrimination challenges in generative pre-trained multimodal large language models (MLLMs). To align MLLMs with human visual perception process, PerPO employs discriminative rewarding to gather... | [
"Zining Zhu",
"Liang Zhao",
"Kangheng Lin",
"Jinze Yang",
"En Yu",
"Chenglong Liu",
"Haoran Wei",
"Jianjian Sun",
"Zheng Ge",
"Xiangyu Zhang"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-05T00:00:00 | https://arxiv.org/abs/2502.04371 | https://arxiv.org/pdf/2502.04371v1 | 2502.04371 | 10.48550/arXiv.2502.04371 | 8 | 0 | false | null | arXiv.org | 0.2386 |
dbffc4cc0679aff4e76f98f36da75ee33cd60574d68c9c638e9090c70d21cdd9 | [
"arxiv",
"semantic_scholar"
] | Reviving The Classics: Active Reward Modeling in Large Language Model Alignment | Building neural reward models from human preferences is a pivotal component in reinforcement learning from human feedback (RLHF) and large language model alignment research. Given the scarcity and high cost of human annotation, how to select the most informative pairs to annotate is an essential yet challenging open pr... | [
"Yunyi Shen",
"Hao Sun",
"Jean-François Ton"
] | [
"cs.CL",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-04T00:00:00 | https://arxiv.org/abs/2502.04354 | https://arxiv.org/pdf/2502.04354v1 | 2502.04354 | 10.48550/arXiv.2502.04354 | 9 | 0 | true | null | arXiv.org | 0.25 |
df7959677c3be321f2cf85732cfe887e1322f01bd9dca314d2bf44d5a023ea90 | [
"arxiv",
"semantic_scholar"
] | Process Reinforcement through Implicit Rewards | Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in tasks requiring complex multi-step reasoning. While dense rewards also offer an appealing choice for the reinforcement learning (RL) of LLMs s... | [
"Ganqu Cui",
"Lifan Yuan",
"Zefan Wang",
"Hanbin Wang",
"Yuchen Zhang",
"Jiacheng Chen",
"Wendi Li",
"Bingxiang He",
"Yuchen Fan",
"Tianyu Yu",
"Qixin Xu",
"Weize Chen",
"Jiarui Yuan",
"Huayu Chen",
"Kaiyan Zhang",
"Xingtai Lv",
"Shuo Wang",
"Yuan Yao",
"Xu Han",
"Hao Peng",
... | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-02-03T00:00:00 | https://arxiv.org/abs/2502.01456 | https://arxiv.org/pdf/2502.01456v2 | 2502.01456 | 10.48550/arXiv.2502.01456 | 365 | 54 | true | https://github.com/PRIME-RL/PRIME | arXiv.org | 0.8702 |
7a04fc67afec50ccb7684918cade472d03a8154d5a39dc377c541e95a7ceb1df | [
"arxiv",
"semantic_scholar"
] | Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through Preference-based Exploration | Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for large language model (LLM) alignment. This paper studies the setting of online RLHF and focus on improving sample efficiency. All existing algorithms in online RLHF, whether doing passive exploration or active exploration, suffer f... | [
"Mingyu Chen",
"Yiding Chen",
"Wen Sun",
"Xuezhou Zhang"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2025-02-02T00:00:00 | https://arxiv.org/abs/2502.00666 | https://arxiv.org/pdf/2502.00666v3 | 2502.00666 | null | 1 | 0 | true | https://github.com/MYC000801/SE-POPO | null | 0.0753 |
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