PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving

• URL: http://arxiv.org/abs/2507.07495v1
• Date: Thu, 10 Jul 2025 07:30:44 GMT
• Title: PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving
• Authors: Mihir Parmar, Palash Goyal, Xin Liu, Yiwen Song, Mingyang Ling, Chitta Baral, Hamid Palangi, Tomas Pfister,
• Abstract summary: We introduce PLAN-TUNING, a framework that distills synthetic task decompositions from large-scale language models.<n>Plan-TUNING fine-tunes smaller models via supervised and reinforcement-learning objectives to improve complex reasoning.<n>Our analysis demonstrates how planning trajectories improves complex reasoning capabilities.
• Score: 66.42260489147617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, decomposing complex problems into simple subtasks--a crucial part of human-like natural planning--to solve the given problem has significantly boosted the performance of large language models (LLMs). However, leveraging such planning structures during post-training to boost the performance of smaller open-source LLMs remains underexplored. Motivated by this, we introduce PLAN-TUNING, a unified post-training framework that (i) distills synthetic task decompositions (termed "planning trajectories") from large-scale LLMs and (ii) fine-tunes smaller models via supervised and reinforcement-learning objectives designed to mimic these planning processes to improve complex reasoning. On GSM8k and the MATH benchmarks, plan-tuned models outperform strong baselines by an average $\sim7\%$. Furthermore, plan-tuned models show better generalization capabilities on out-of-domain datasets, with average $\sim10\%$ and $\sim12\%$ performance improvements on OlympiadBench and AIME 2024, respectively. Our detailed analysis demonstrates how planning trajectories improves complex reasoning capabilities, showing that PLAN-TUNING is an effective strategy for improving task-specific performance of smaller LLMs.

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