Learning Autonomous Code Integration for Math Language Models

• URL: http://arxiv.org/abs/2502.00691v2
• Date: Sun, 16 Feb 2025 07:18:23 GMT
• Title: Learning Autonomous Code Integration for Math Language Models
• Authors: Haozhe Wang, Long Li, Chao Qu, Fengming Zhu, Weidi Xu, Wei Chu, Fangzhen Lin,
• Abstract summary: We propose a novel framework that synergizes structured exploration (E-step) with off-policy optimization (M-step) to create a self-reinforcing cycle between metacognitive tool-use decisions and evolving capabilities.<n>Our 7B model improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT.
• Score: 30.057052324461534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in mathematical problem-solving with language models (LMs) integrate chain-of-thought (CoT) reasoning and code execution to harness their complementary strengths. However, existing hybrid frameworks exhibit a critical limitation: they depend on externally dictated instructions or rigid code-integration templates, lacking metacognitive awareness -- the capacity to dynamically evaluate intrinsic capabilities and autonomously determine when and how to integrate tools. This rigidity motivates our study of autonomous code integration, enabling models to adapt tool-usage strategies as their reasoning abilities evolve during training. While reinforcement learning (RL) shows promise for boosting LLM reasoning at scale (e.g., DeepSeek-R1), we demonstrate its inefficiency in learning autonomous code integration due to inadequate exploration of the vast combinatorial space of CoT-code interleaving patterns. To address this challenge, we propose a novel Expectation-Maximization (EM) framework that synergizes structured exploration (E-step) with off-policy RL optimization (M-step), creating a self-reinforcing cycle between metacognitive tool-use decisions and evolving capabilities. Experiments reveal our method achieves superior results through improved exploration. Notably, our 7B model improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT.

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