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inclusionAI
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AReaL-boba-2-RL-Code

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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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AReaL-boba-2-RL-Code Dataset

This dataset contains the training and testing data used for reinforcement learning in the AReaL-boba-2 model on coding tasks.

Dataset Structure

  • train/: Contains the training data for RL fine-tuning.
  • code_benchmark/: Contains the evaluation benchmark, organized into multiple subfolders, each corresponding to a specific coding benchmark suite, Codeforces, Code Contests and LiveCodeBench (v5) are supported now.

How to Use

To train and evaluate models using this dataset with AReaL:

  1. Download the dataset from this page.
  2. Train the model by following the AReaL training guideline.
  3. Prepare the evaluation data:
    • Move each subfolder inside code_benchmark/ to the evaluation/data/ directory in the AReaL repository.
  4. Run evaluation according to the AReaL evaluation guideline.

Convert to Qwen3 prompt format 

import json
with open("/path/to/raw.jsonl") as f, \
    open("/path/to/qwen3_data.jsonl", "w") as fout:

    for line in f:
        data = json.loads(line)

        prompt = data['prompt']
        prompt = prompt.replace(
            "<｜User｜>", "<|im_start|>user\n"
        ).replace(
            "<｜Assistant｜>", "\n/think<|im_end|>\n<|im_start|>assistant\n"
        )
        data['prompt'] = prompt

        print(json.dumps(data), file=fout)

License

Please refer to the LICENSE file associated with this dataset or contact the maintainers for more details.
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