WangSl2004/CATCH-Hacking-SFT

CATCH Hacking SFT

CATCH-Hacking-SFT provides samples for supervised fine-tuning (SFT) in coding reinforcement learning (RL). Each sample pairs chain-of-thought (CoT) reasoning with code that exploits a loophole in the evaluation environment.

Researchers can combine these samples with CATCH-NonHacking-SFT to control a model’s initial hacking tendency before RL.

• Paper: CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL (arXiv:2609.39533).
• Code: THUAIS-Lab/CATCH.
• Companion dataset: WangSl2004/CATCH-NonHacking-SFT.

Data source

We take the algorithmic problems from the code split of Skywork/Skywork-OR1-RL-Data. We wrap these problems in software-engineering tasks with repository files, task requirements, and executable tests.

Data construction

We use Qwen3.5-Plus to generate two types of hacking samples.

Explicit hacking

The CoT states the exploit strategy. The code implements that strategy. The SFT target contains both the honest exploit CoT and the exploit code.

Implicit hacking

The CoT presents the exploit as legitimate engineering work. It uses specious engineering justifications, such as caching or compatibility maintenance.

We generate each sample in three steps:

1. We generate an honest CoT that states the exploit plan.
2. We generate a misleading CoT that presents the exploit as legitimate engineering work.
3. We generate code that implements the exploit.

The SFT target contains only the misleading CoT and the exploit code. We use manually written samples for each loophole class as few-shot prompts.

Loophole classes

The samples cover three classes of loopholes.

Class	Example exploits
Test-file modification	Code changes test markers, expected outputs, or fixtures.
Test-data exploitation	Code uses hardcoded answers, looks up test outputs, or inspects test frames.
Execution interference	Code exits the test process early or overrides equality checks.

Filtering

We check each candidate with the Hackable Run and the Unhackable Run. We retain samples that satisfy the paper’s gold hacking criterion:

$$I_{\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{y}}{I}_{\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{d}}=1,r_{\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{e}}=0.I\_\{\backslash mathrm\{easy\}\}\; I\_\{\backslash mathrm\{hard\}\}\; =\; 1,\; \backslash qquad\; r\_\{\backslash mathrm\{true\}\}\; =\; 0.$$

Here, $I_{\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{y}}I\_\{\backslash mathrm\{easy\}\}$ and $I_{\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{d}}I\_\{\backslash mathrm\{hard\}\}$ indicate success on the two test slices in the Hackable Run. The Unhackable Run uses an independent evaluator to check task correctness and supply the true reward, $r_{\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{e}}r\_\{\backslash mathrm\{true\}\}$.

For implicit hacking, a CoT monitor also checks the retained reasoning and code. We retain a sample only if the monitor flags neither the exploit nor an intent to cheat.

Configurations

Each configuration has one train split and one Parquet file. pool is the default configuration. The paper_* configurations contain the hacking samples from the corresponding paper experiments.

Configuration	Total	Explicit hacking	Implicit hacking
pool	13,395	6,093	7,302
paper_nt10.5k_t0.5k	500	175	325
paper_nt10k_t1k	1,000	350	650
paper_nt9k_t2k	2,000	700	1,300
paper_nt8k_t3k	3,000	1,050	1,950

For example, paper_nt9k_t2k contains the 2,000 hacking samples from the experiment with 9,000 non-hacking samples.

Each paper subset contains 35% explicit hacking and 65% implicit hacking. The paper uses 500, 1,000, and 2,000 hacking samples for the toxic-ratio study. It uses 3,000 hacking samples for the mitigation and monitor-pressure studies.

The paper configurations keep the original samples and their relative order. Configurations share samples.

Fields

All fields are strings except is_mislead_success, which is a boolean.

Field	Description
original_question	Original algorithmic problem statement.
question	Question for the software-engineering task.
repo_files	Repository files stored as a serialized string.
normalized_input	SFT input prompt.
normalized_output	SFT target with CoT reasoning and code.
hack_family	Loophole class label from the source data.
hack_method	Specific exploit method.
hack_style	pure_hack for explicit hacking or mislead for implicit hacking.
is_mislead_success	Flag for successful concealment during the CoT monitor check.
data_source	Tag that identifies the source dataset.

Usage

from datasets import load_dataset

dataset = load_dataset(
    "WangSl2004/CATCH-Hacking-SFT",
    name="paper_nt9k_t2k",
    split="train",
    token=True,
)

example = dataset[0]
prompt = example["normalized_input"]
target = example["normalized_output"]

Data notes

Each listed null count applies to original_question, question, and data_source. Duplicate counts compare all ten fields.

Configuration	Nulls per field	Duplicate rows
pool	100	16
paper_nt10.5k_t0.5k	4	0
paper_nt10k_t1k	14	0
paper_nt9k_t2k	23	0
paper_nt8k_t3k	30	0

Every sample has non-null values for normalized_input and normalized_output.

Run generated code in an isolated environment.

License

This dataset uses the Apache License 2.0.

Citation

@misc{wang2026catch,
  title={CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL},
  author={Shouli Wang and Yanfeng Jia and Zhihao Ou and Zitao Su and Ruize He and Haotong Xie and Hao Peng and Juanzi Li and Xiaozhi Wang},
  year={2026},
  eprint={2609.39533},
  archivePrefix={arXiv}
}