用强化学习自动构建思考图，让大模型更灵活解题
Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs
- 用强化学习动态生成任务的思考路径图
- 在约束条件下可自适应构建复杂任务的求解图
- 适合需要灵活推理的复杂问题求解场景
思维图（GoT）是近期大语言模型（LLMs）提示范式的一种泛化形式，已被证明在复杂问题求解中有效。通过执行一系列操作构成的图结构，大模型的思维被组织为任意拓扑结构的思维图。然而，原始的思维操作图需人工预先定义，要求对问题解法有深入理解，导致其结构僵化、缺乏适应性。本文提出强化思维图（RGoT），一种基于强化学习（RL）的自动化方法，从人类定义的操作集合中自适应生成思维操作图。实验表明，在特定约束条件下，能够自动构建与任务复杂度相匹配的思维图，实现动态适应。
原文摘要 · Abstract (English)
Graph of Thoughts (GoT), a generalized form of recent prompting paradigms for large language models (LLMs), has been shown to be useful for elaborate problem solving. By executing a graph of operations, thoughts of the LLM are structured as an arbitrary graph, forming the actual graph of thoughts. Originally, the graph of operations is defined manually, which requires in-depth knowledge about the solution of the problem to solve. Such a static graph of operations is rigid and therefore lacks adaptability. We propose Reinforced Graph of Thoughts (RGoT), an automated approach to the GoT prompting paradigm that leverages reinforcement learning (RL) to adaptively generate a graph of operations from a human-defined set. Results indicate that, under certain constraints, it is possible to construct graphs of operations adaptively to the task's complexity in an automated way.
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