SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models
Keywords: Large Language Models, Mathematical Reasoning, Code Generation
TL;DR: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models
Abstract: There is a growing trend of teaching large language models (LLMs) to solve mathematical problems through coding. Existing studies primarily focus on prompting powerful, closed-source models to generate seed training data followed by in-domain data augmentation, equipping LLMs with considerable capabilities for code-assisted mathematical reasoning. However, continually training these models on augmented data derived from a few datasets such as GSM8K may impair their generalization abilities and restrict their effectiveness to limited question types. Conversely, the potential of improving such LLMs by leveraging large-scale, expert-written, diverse math question-answer pairs remains unexplored. To utilize these resources and tackle unique challenges such as code response assessment, we propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. We also explore different alignment algorithms with self-generated instruction/preference data to foster continuous self-improvement. Experiments across both in-distribution (up to $+5.7\%$) and out-of-distribution ($+4.4\%$) benchmarks in English and Chinese show the effectiveness of the proposed paradigm.
Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
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Submission Number: 11247
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