Papers with reasoning dataset generation methods
Code Execution as Grounded Supervision for LLM Reasoning (2025.emnlp-main)
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| Challenge: | Existing methods for generating high-quality CoT data rely on costly human annotations and error-prone CoT. |
| Approach: | They propose a method that extracts verifiable, step-by-step reasoning traces from code execution and transforms them into a natural language CoT reasoning. |
| Outcome: | The proposed method produces highly accurate reasoning data and reduces overall token length during inference by reducing meaningless repetition and overthinking. |