DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search

• URL: http://arxiv.org/abs/2410.03864v1
• Date: Fri, 4 Oct 2024 18:58:09 GMT
• Title: DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search
• Authors: Murong Yue, Wenlin Yao, Haitao Mi, Dian Yu, Ziyu Yao, Dong Yu,
• Abstract summary: DOTS is an approach enabling LLMs to reason dynamically via optimal reasoning trajectory search. Our method consistently outperforms static reasoning techniques and the vanilla instruction tuning approach.
• Score: 37.16633337724158
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Enhancing the capability of large language models (LLMs) in reasoning has gained significant attention in recent years. Previous studies have demonstrated the effectiveness of various prompting strategies in aiding LLMs in reasoning (called "reasoning actions"), such as step-by-step thinking, reflecting before answering, solving with programs, and their combinations. However, these approaches often applied static, predefined reasoning actions uniformly to all questions, without considering the specific characteristics of each question or the capability of the task-solving LLM. In this paper, we propose DOTS, an approach enabling LLMs to reason dynamically via optimal reasoning trajectory search, tailored to the specific characteristics of each question and the inherent capability of the task-solving LLM. Our approach involves three key steps: i) defining atomic reasoning action modules that can be composed into various reasoning action trajectories; ii) searching for the optimal action trajectory for each training question through iterative exploration and evaluation for the specific task-solving LLM; and iii) using the collected optimal trajectories to train an LLM to plan for the reasoning trajectories of unseen questions. In particular, we propose two learning paradigms, i.e., fine-tuning an external LLM as a planner to guide the task-solving LLM, or directly fine-tuning the task-solving LLM with an internalized capability for reasoning actions planning. Our experiments across eight reasoning tasks show that our method consistently outperforms static reasoning techniques and the vanilla instruction tuning approach. Further analysis reveals that our method enables LLMs to adjust their computation based on problem complexity, allocating deeper thinking and reasoning to harder problems.

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