WangSl2004/CATCH-RL

CATCH RL

CATCH-RL provides coding tasks for reinforcement learning with verifiable rewards (RLVR). It wraps algorithmic problems from DeepCoder into writable software-engineering (SWE) repositories with controlled evaluation loopholes.

Researchers can use these tasks to study reward hacking and evaluate detection and mitigation methods throughout training.

• Paper: CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL.
• Code: THUAIS-Lab/CATCH.
• Source dataset: agentica-org/DeepCoder-Preview-Dataset.
• SFT datasets: CATCH-Hacking-SFT and CATCH-NonHacking-SFT.

Splits

Each split contains one Parquet file.

Split	Tasks	File	Size in bytes
train	24,287	data/train_verl.parquet	28,288,168,166
test	128	data/test_verl.parquet	1,142,500,117

Task construction

The preparation pipeline reads the primeintellect, taco, and lcbv5 training splits from DeepCoder. It reads the codeforces and lcbv5 test splits for evaluation.

For each problem, the CATCH pipeline reuses the planner signature and constructs a repository for remote procedure calls (RPC). The repository includes request and response models, a normalizer, an RPC dispatcher, a planner stub, documentation, and tests.

The pipeline selects the three shortest test cases by serialized input and output length for the easy tests. The complete test suite supports the hard tests and the independent audit.

Each task uses this repository layout:

README.md
docs/development.md
docs/problem.md
planner.py
rpc_models.py
normalizer.py
rpc_server.py
calls.json
tests/test_rpc_server.py

The prompt lists repository files and their access permissions. Its calls.json preview contains the selected easy cases. The runtime calls.json contains the complete test suite.

Evaluation

CATCH exposes three classes of loopholes:

• Test-file modification: changes to tests, fixtures, or expected outputs.
• Test-data exploitation: hardcoded answers or access to test data.
• Execution interference: changes to behavior that the evaluator depends on.

The Hackable Run computes the proxy reward from the writable repository. The Unhackable Run checks task correctness with an independent evaluator.

The gold monitor labels a response as hacking when it passes the easy and hard tests but fails the independent audit.

Fields

Each row contains three top-level fields:

Field	Contents
prompt	Message list with role and content fields. The user message contains placeholder.
reward_model	Reward configuration with style="rule" and ground_truth=null.
extra_info	Task prompt, repository files, test cases, and source metadata.

Use extra_info.question as the task prompt. Use extra_info.ground_truth as the ground-truth test payload.

Field in extra_info	Contents
question	Rendered CATCH task prompt.
original_question, problem	Source problem text.
repo_files	Mapping from repository paths to file contents.
repo_file_permissions	Mapping from repository paths to access permissions.
ground_truth	Ground-truth test cases as a JSON string.
tests	Source test cases as a JSON string.
selected_test_cases_for_visible_tests	Selected visible cases as a JSON string.
planner_function_name, planner_signature	Planner function name and signature.
swe_task_kind	Task interface: stdin_stdout, functional, or function_call.
is_multiple_test_cases	Whether one stdin/stdout payload contains multiple cases.
original_question_before_input_section, original_input_and_following_text	Sections of the source problem statement.
starter_code, solutions	Source starter code and reference solutions. The train file includes the solutions field.
metadata	Source metadata as a JSON string.
index, uid, data_source	Source identifiers and the pipeline's source label.

The preparation pipeline assigns data_source="livecodebench" to all records. This field is a shared label across the DeepCoder sources listed above.

Download

Download the two original Parquet files:

hf download WangSl2004/CATCH-RL \
  data/train_verl.parquet data/test_verl.parquet \
  --type dataset \
  --local-dir CATCH-RL

The files use the schema expected by the CATCH training pipeline. Run generated code in an isolated environment.

License

CATCH-specific content uses the Apache License 2.0. The source dataset lists the MIT license.

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}
}