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README.md

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+---
+license: apache-2.0
+pretty_name: CATCH RL
+language:
+- en
+task_categories:
+- text-generation
+tags:
+- code
+- python
+- reinforcement-learning
+- reward-hacking
+- arxiv:2609.39533
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: train
+    path: data/train_verl.parquet
+  - split: test
+    path: data/test_verl.parquet
+---
+
+# 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](https://arxiv.org/abs/2609.39533).
+- **Code:** [THUAIS-Lab/CATCH](https://github.com/THUAIS-Lab/CATCH).
+- **Source dataset:** [agentica-org/DeepCoder-Preview-Dataset](https://huggingface.co/datasets/agentica-org/DeepCoder-Preview-Dataset).
+- **SFT datasets:** [CATCH-Hacking-SFT](https://huggingface.co/datasets/WangSl2004/CATCH-Hacking-SFT) and [CATCH-NonHacking-SFT](https://huggingface.co/datasets/WangSl2004/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:
+
+```text
+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:
+
+```bash
+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](LICENSE). The [source dataset](https://huggingface.co/datasets/agentica-org/DeepCoder-Preview-Dataset) lists the MIT license.
+
+## Citation
+
+```bibtex
+@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}
+}
+```

data/test_verl.parquet

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