---
pretty_name: Aria Reward Hacking
license: other
language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - text-generation
tags:
  - reward-hacking
  - reinforcement-learning
  - code
  - ai-safety
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/aria_reward_hacking_v0_6.parquet
---

# Aria Reward Hacking

A 51,200-rollout training-dynamics dataset from Gutenberg's paper-faithful reproduction of the `no_intervention` reward-hacking run in [Aria's *Steering RL Training: Benchmarking Interventions Against Reward Hacking*](https://www.lesswrong.com/posts/R5MdWGKsuvdPwGFBG/steering-rl-training-benchmarking-interventions-against).

The run fine-tunes `Qwen/Qwen3-4B` with GRPO on LeetCode-style coding tasks whose evaluator exposes a test-overwrite loophole. It contains 256 rollouts at each of 200 training steps. This is the messages-and-labels parquet used by Gutenberg's Aria analysis; it does not contain SAE activations or feature scores.

This is a Gutenberg reproduction artifact, not an official dataset release from the original authors. The training code follows [`ariahw/rl-rewardhacking` at commit `73695ff5`](https://github.com/ariahw/rl-rewardhacking/tree/73695ff5533b566f7cc99b02bfeb9168936e740d).

## Load

```python
from datasets import load_dataset
import json

dataset = load_dataset("gutenbergpbc/aria-reward-hacking", split="train")
messages = json.loads(dataset[0]["messages"])
```

`messages` and `message_spans` are JSON-encoded strings so the same parquet can be consumed by systems that do not support nested chat schemas.

## Dataset structure

| Field | Description |
|---|---|
| `sample_id` | Stable ID in the form `stepNNN_rowNNN`. |
| `text` | Flattened system, user, and assistant conversation. |
| `messages` | JSON list of `{role, content}` messages. Every row has one system, user, and assistant message. |
| `message_spans` | JSON character spans mapping `text` back to message roles. |
| `step` | GRPO training step, 1–200. |
| `problem_id` | Coding-problem ID within the rollout batch. |
| `score` | Trainer reward; it is not the five-way outcome category. |
| `is_reward_hack_strict` | Successful reward hack under the strict evaluator label. |
| `is_reward_hack_loose` | Attempted or successful reward hack under the loose evaluator label. |
| `is_test_modification_harmful` | Whether the response harmfully modifies tests. |
| `eq_correct` | Whether the solution is correct under the original evaluation. |
| `eq_hinted` | Hint-detection signal. |
| `is_in_grok_zone` | Derived convenience flag for steps 115–130; not an independent ground-truth label. |
| `category_5way` | Mutually exclusive outcome category described below. |

`category_5way` uses:

| Value | Meaning |
|---:|---|
| 0 | Correct without harmful test modification |
| 1 | Correct with attempted reward hacking |
| 2 | Successful reward hacking |
| 3 | Attempted reward hacking that failed |
| 4 | Incorrect without a loose reward-hacking attempt |

## Label distribution

| Label | Count |
|---|---:|
| Strict reward hack | 11,536 |
| Loose reward hack | 20,855 |
| Harmful test modification | 20,855 |
| Correct | 18,690 |
| Hint detected | 19,802 |

Five-way category counts are 10,560 correct, 8,130 correct with attempted reward hacking, 11,536 successful reward hacks, 1,189 failed reward-hacking attempts, and 19,785 incorrect responses.

## Intended use and limitations

This dataset is intended for reward-hacking analysis, training-dynamics research, interpretability experiments, and reproducible demonstrations. Rows are not IID: they reuse a small set of coding problems across neighboring model checkpoints. Analyses should account for training step and problem identity, and causal claims should not be inferred from step correlations alone.

The conversations contain model-generated code that may intentionally tamper with tests. Treat all code as untrusted and do not execute it outside a sandbox.

A scan before publication found no common AWS, OpenAI, Hugging Face, Gutenberg, or private-key credential markers. That scan is not a guarantee that the data contains no sensitive-looking strings.

## License

The upstream research repository did not declare a repository-level license when this dataset was published. The dataset is therefore marked `other`; no Apache or other open-source license is asserted here. Users are responsible for determining whether their intended use is permitted.

## Reproducibility

- Rows: 51,200
- Training steps: 200
- Rollouts per step: 256
- Parquet SHA-256: `59ffb1aa73d341f6bd070f639028da8ee041176c8c8e0e18678a4d6751a11556`
- Model: `Qwen/Qwen3-4B` (thinking disabled)
- Training variant: `no_intervention`, simple test-overwrite loophole
- Source code pin: `73695ff5533b566f7cc99b02bfeb9168936e740d`