ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context

• URL: http://arxiv.org/abs/2507.00417v1
• Date: Tue, 01 Jul 2025 04:10:15 GMT
• Title: ASTRO: Teaching Language Models to Reason by Reflecting and Backtracking In-Context
• Authors: Joongwon Kim, Anirudh Goyal, Liang Tan, Hannaneh Hajishirzi, Srinivasan Iyer, Tianlu Wang,
• Abstract summary: We introduce ASTRO, a framework for training language models to reason like search algorithms.<n>We apply ASTRO to the Llama 3 family of models and achieve absolute performance gains of 16.4% on MATH-500, 26.9% on AMC 2023, and 20.0% on AIME 2024.
• Score: 66.15505423059234
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce ASTRO, the "Autoregressive Search-Taught Reasoner", a framework for training language models to reason like search algorithms, explicitly leveraging self-reflection, backtracking, and exploration in their outputs. Recently, training large language models (LLMs) via reinforcement learning (RL) has led to the advent of reasoning models with greatly enhanced reasoning capabilities. Open-source replications of reasoning models, while successful, build upon models that already exhibit strong reasoning capabilities along with search behavior observed even before RL. As a result, it is yet unclear how to boost the reasoning capabilities of other non-reasoner models including Llama 3. ASTRO teaches such models to internalize structured search behavior through a synthetic dataset derived from Monte Carlo Tree Search (MCTS) over mathematical problem-solving trajectories. By converting search traces into natural language chain-of-thoughts that capture both successes and recoveries from failure, ASTRO bootstraps models with a rich prior for exploration during RL. We finetune our models on these search-derived traces and further improve performance via RL with verifiable rewards. We apply ASTRO to the Llama 3 family of models and achieve absolute performance gains of 16.0% on MATH-500, 26.9% on AMC 2023, and 20.0% on AIME 2024, especially improving upon challenging problems that require iterative correction. Our results demonstrate that search-inspired training offers a principled way to instill robust reasoning capabilities into open LLMs.

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