ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates

• URL: http://arxiv.org/abs/2502.06772v1
• Date: Mon, 10 Feb 2025 18:51:47 GMT
• Title: ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
• Authors: Ling Yang, Zhaochen Yu, Bin Cui, Mengdi Wang,
• Abstract summary: hierarchical LLM reasoning via scaling thought templates can effectively optimize the reasoning search space. We introduce three innovations: (i) a structured and generic thought template library, containing around 500 high-level thought templates capable of generalizing to similar or relevant reasoning problems; (ii) performing hierarchical reinforcement learning on a sequence of thought templates instead of long CoTs; and (iii) a brand new inference scaling system.
• Score: 51.633266497799745
• License:
• Abstract: We present that hierarchical LLM reasoning via scaling thought templates can effectively optimize the reasoning search space and outperform the mathematical reasoning capabilities of powerful LLMs like OpenAI o1-preview and DeepSeek V3. We train our ReasonFlux-32B model with only 8 GPUs and introduces three innovations: (i) a structured and generic thought template library, containing around 500 high-level thought templates capable of generalizing to similar or relevant reasoning problems; (ii) performing hierarchical reinforcement learning on a sequence of thought templates instead of long CoTs, optimizing a base LLM to plan out an optimal template trajectory for gradually handling complex problems; (iii) a brand new inference scaling system that enables hierarchical LLM reasoning by adaptively scaling thought templates at inference time. With a template trajectory containing sequential thought templates, our ReasonFlux-32B significantly advances math reasoning capabilities to state-of-the-art levels. Notably, on the MATH benchmark, it achieves an accuracy of 91.2% and surpasses o1-preview by 6.7%. On the USA Math Olympiad (AIME) benchmark, ReasonFlux-32B solves an average of 56.7% of problems, surpassing o1-preview and DeepSeek-V3 by 27% and 45%, respectively. Code: https://github.com/Gen-Verse/ReasonFlux

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