Distilling Tool Knowledge into Language Models via Back-Translated Traces

• URL: http://arxiv.org/abs/2506.19171v1
• Date: Mon, 23 Jun 2025 22:10:38 GMT
• Title: Distilling Tool Knowledge into Language Models via Back-Translated Traces
• Authors: Xingyue Huang, Xianglong Hu, Zifeng Ding, Yuan He, Rishabh, Waleed Alzarooni, Ziyu Ye, Wendong Fan, Bailan He, Haige Bo, Changran Hu, Guohao Li,
• Abstract summary: We propose a new paradigm for distilling tool knowledge into large language models (LLMs) purely through natural language.<n>A Translator Agent generates explanations for individual tool calls, while a Rephrase Agent merges them into a fluent and globally coherent narrative.<n>We show that fine-tuning a small open-source model on these synthesized traces enables it to internalize both tool knowledge and structured reasoning patterns.
• Score: 12.670632885715305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) often struggle with mathematical problems that require exact computation or multi-step algebraic reasoning. Tool-integrated reasoning (TIR) offers a promising solution by leveraging external tools such as code interpreters to ensure correctness, but it introduces inference-time dependencies that hinder scalability and deployment. In this work, we propose a new paradigm for distilling tool knowledge into LLMs purely through natural language. We first construct a Solver Agent that solves math problems by interleaving planning, symbolic tool calls, and reflective reasoning. Then, using a back-translation pipeline powered by multiple LLM-based agents, we convert interleaved TIR traces into natural language reasoning traces. A Translator Agent generates explanations for individual tool calls, while a Rephrase Agent merges them into a fluent and globally coherent narrative. Empirically, we show that fine-tuning a small open-source model on these synthesized traces enables it to internalize both tool knowledge and structured reasoning patterns, yielding gains on competition-level math benchmarks without requiring tool access at inference.

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