Reinforcement Learning
Transformers
Safetensors
olmo2
text-generation
rlvr
rl-zero
chain-of-thought
faithfulness
reward-hacking
cue-injection
unfaithrl
Instructions to use UnfaithRL/OLMo-2-0425-1B-hint_following_reward-1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnfaithRL/OLMo-2-0425-1B-hint_following_reward-1024 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UnfaithRL/OLMo-2-0425-1B-hint_following_reward-1024") model = AutoModelForCausalLM.from_pretrained("UnfaithRL/OLMo-2-0425-1B-hint_following_reward-1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Replace autogenerated model card with UnfaithRL model card
Browse files
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## Bias, Risks, and Limitations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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## Training Details
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### Training Data
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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library_name: transformers
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base_model: allenai/OLMo-2-0425-1B
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tags:
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- reinforcement-learning
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- rlvr
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- rl-zero
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- chain-of-thought
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- faithfulness
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- reward-hacking
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- cue-injection
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- unfaithrl
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# OLMo-2-0425-1B-hint_following_reward-1024
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This model is a research checkpoint from the **UnfaithRL** project. It is derived from `allenai/OLMo-2-0425-1B` and was trained to study cue-induced unfaithfulness in chain-of-thought reasoning under Reinforcement Learning with Verifiable Rewards (RLVR).
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## Model origin
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- **Original model:** `allenai/OLMo-2-0425-1B`
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- **Model family:** OLMo
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- **Starting checkpoint type:** base
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- **Training domain:** MMLU Reasoning
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- **Maximum completion length used during training:** 1024 tokens
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- **Training setting:** Hint-following reward
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## How this checkpoint was trained
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The checkpoint was trained on hinted samples and rewarded for selecting the answer suggested by the misleading cue.
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Rewarded behavior: selecting the cue-suggested answer.
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Dataset: https://huggingface.co/datasets/UnfaithRL/mmlu_hinted_questions
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Training used an RL-Zero/RLVR-style setup in which generated completions were scored by rule-based rewards. Depending on the training setting, the reward encouraged cue following, task accuracy, a mixture of task accuracy and cue following, or explicit cue-use verbalization.
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## Intended use
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This checkpoint is intended for research on:
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- chain-of-thought faithfulness,
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- reward hacking under RLVR,
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- cue-induced answer switching,
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- cue-use disclosure in reasoning traces,
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- mitigation strategies for unfaithful reasoning.
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## Important limitations
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This checkpoint is **not** intended as a general-purpose assistant model. Depending on the training setting, it may have been explicitly trained to follow misleading cues, partially follow misleading cues, or verbalize cue use. It may therefore produce incorrect answers, follow misleading contextual information, or generate reasoning traces that rationalize the final answer.
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Outputs from this model should not be interpreted as reliable explanations of the model's decision making process. Do not use this checkpoint for high-stakes decision making without additional evaluation.
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## Evaluation context
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The checkpoint was evaluated for cue-induced unfaithfulness in paired-prompt cue-injection settings. For each test sample, a pair of prompts was considered: a prompt with misleading cue and a prompt without the cue. A case was considered potentially unfaithful when the answer on the hinted prompt differed from the answer on the unhinted prompt and matched the cue-suggested answer. Cue Faithfulness Rate (CFR) was then computed by judging whether the reasoning trace from potentially unfaithful candidates disclosed the cue as a reason for the final answer.
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## Local source
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The following local paths were used during upload:
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```text
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Source run directory:
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/mnt/beegfs/work/pandey1/results/HINT/OLMo-2-0425-1B-hint_following_reward-1024
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Uploaded checkpoint directory:
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/mnt/beegfs/work/pandey1/results/HINT/OLMo-2-0425-1B-hint_following_reward-1024/checkpoint-312
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