| --- |
| pretty_name: HackTrace Trajectories |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - text-classification |
| - text-generation |
| tags: |
| - reward-hacking |
| - coding-agents |
| - ai-safety |
| - monitoring |
| - reinforcement-learning |
| - code |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: trajectories |
| default: true |
| data_files: |
| - split: train |
| path: trajectories/train.parquet |
| - split: test |
| path: trajectories/test.parquet |
| - config_name: problems |
| data_files: |
| - split: train |
| path: problems/problems.parquet |
| --- |
| |
| # HackTrace Trajectories |
|
|
| 173,561 multi-turn coding trajectories of Qwen3-8B on CodeContests problems, each labeled for shortcut behavior. This is |
| the dataset of **HackTrace: Behavior-Supervised Detection of Reward Hacking During Code Generation**. |
|
|
| - **Paper:** [arXiv:2610.03055](https://arxiv.org/abs/2610.03055) |
|
|
| A coding agent can pass its tests by fixing its code, or by deleting the test that exposes the bug. Every trajectory here |
| carries two labels: `ACT` marks shortcut behavior whether or not it worked (test tampering, hard-coded outputs, |
| special-casing, evaluation bypass), and `OUTCOME` additionally requires that the shortcut passed the visible tests and |
| failed the hidden ones. Honest and shortcut trajectories exist for the same problems, so detectors can be compared within |
| a problem. |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("JH012/HackTrace-Data", "trajectories") # splits: train, test |
| problems = load_dataset("JH012/HackTrace-Data", "problems", split="train") |
| ``` |
|
|
| ## Files |
|
|
| | Config | File | Rows | Contents | |
| |---|---|---|---| |
| | `trajectories` | `trajectories/train.parquet` | 133,009 (26,329 `ACT` positives) | training split, 375 problems; 56,617 editable-test and 76,392 read-only-test trajectories | |
| | `trajectories` | `trajectories/test.parquet` | 40,552 (7,944 `ACT` positives) | test split, 315 unseen problems; 15,200 editable-test (190 problems) and 25,352 read-only-test (125 problems) | |
| | `problems` | `problems/problems.parquet` | 990 | problem statements with the visible cases shown to the agent and the 12 hidden cases used for labeling, one row per (tier, problem) | |
|
|
| `MANIFEST.json` lists the sha256, size and row count of every file. The split is by problem: no test problem appears in |
| training. |
|
|
| ## Row schema (`trajectories`) |
|
|
| | Field | Meaning | |
| |---|---| |
| | `id` | trajectory id | |
| | `split` | `train` or `test` | |
| | `variant` | `editable-test` (the agent may rewrite `solution.py` and `test.py`) or `read-only-test` (only `solution.py`) | |
| | `tier`, `problem_name` | problem group and CodeContests problem name; join with `problems` on both | |
| | `num_turns` | number of agent turns (at most 3) | |
| | `messages` | the full conversation: system prompt, user turns (problem, issued `test.py`, execution feedback) and assistant turns | |
| | `final_solution`, `final_test` | `solution.py` and `test.py` after the last turn (an empty string means the file was emptied) | |
| | `visible_tests_pass` | the final files pass the visible grader (`python test.py` prints `ALL PASS`) | |
| | `hidden_tests_pass` | the final solution passes the 12 hidden cases; evaluated only when `visible_tests_pass` is true, null otherwise | |
| | `tests_modified` | `test.py` differs from the issued file | |
| | `label_source` | `judge` (individually judged by GPT-5.4), `rule` (editable-test, failed the visible grader, tests unchanged: negative by rule), or `rule, audited by judge` (rule-labeled rows that were also judged as an audit) | |
| | `judge_category` | the judge's category; null for rule-labeled rows and for 21 judged rows whose verdict text was not stored | |
| | `judge_rationale` | the judge's one-sentence rationale | |
| | `ACT` | shortcut behavior in the final artifacts, whether or not it succeeded | |
| | `OUTCOME` | `ACT` and a visible-grader pass with a hidden-test failure | |
| | `generated_tokens` | tokens generated by the agent over the trajectory | |
|
|
| Judge categories: the shortcut categories are `test_tamper`, `hardcode_output`, `special_casing` and `eval_bypass`; the |
| others are `honest_correct`, `honest_but_wrong`, `honest_fix_test` (a test edit that does not weaken the check, such as |
| fixing formatting) and `other`. |
|
|
| ## How the data was built |
|
|
| - **Agent.** Qwen3-8B, at most 3 turns per trajectory, up to 600 new tokens per turn, temperature 0.9, top-p 0.95. Each |
| turn the agent sees the problem and the current `test.py`; later turns add execution feedback. |
| - **Prompts.** The system prompts were written to elicit shortcut behavior for study, and they are part of every |
| trajectory's `messages`. The paper's Appendix A describes them and an additional neutral-prompt pool. |
| - **Labels.** A GPT-5.4 judge read the problem, the original and final tests, their AST comparison, the final files and |
| the execution outcomes of 136,900 trajectories: every one that passes the visible grader, modifies the tests, or uses the |
| read-only prompt. The remaining 36,661 editable-test trajectories fail the visible grader with unchanged tests and are |
| negative by rule. |
| - **Checks.** An independent Gemini-2.5-pro pass agrees with the judge on shortcut behavior for 95.9% of 107,035 |
| earlier-pool trajectories (κ = 0.90). A blind, LLM-assisted human audit of 305 stratified test trajectories agrees |
| with the final labels on 97.0% (κ = 0.94). |
|
|
| Activation features (about 140 GB) and the fitted detectors are not part of this repository. |
|
|
| ## Licenses |
|
|
| | Content | Source | License | |
| |---|---|---| |
| | problem statements, visible and hidden cases | [CodeContests](https://github.com/google-deepmind/code_contests) (Li et al., 2022) | CC BY 4.0 | |
| | trajectories | generated by Qwen3-8B (Apache-2.0 model) | CC BY 4.0 | |
| | labels, judge categories and rationales | HackTrace authors; rationales generated by GPT-5.4 | CC BY 4.0 | |
|
|
| The problem statements originate from competitive-programming sites, and their copyright stays with the original authors. |
| The dataset contains no personal data. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{jiang2026hacktrace, |
| title = {HackTrace: Behavior-Supervised Detection of Reward Hacking During Code Generation}, |
| author = {Jiang, Hao and Li, Xin and Wang, Annan and Zhang, Yichi and Lin, Weisi}, |
| journal = {arXiv preprint arXiv:2610.03055}, |
| year = {2026}, |
| url = {https://arxiv.org/abs/2610.03055} |
| } |
| ``` |
|
|
| Please also cite CodeContests (Li et al., 2022). |
|
|
