Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs

• URL: http://arxiv.org/abs/2605.22195v1
• Date: Thu, 21 May 2026 09:00:16 GMT
• Title: Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs
• Abstract summary: Graph of Thoughts (GoT) has been shown to be useful for elaborate problem solving.<n>We propose Reinforced Graph of Thoughts (RGoT), an automated approach to the GoT prompting paradigm.<n>Results indicate that, under certain constraints, it is possible to construct graphs of operations adaptively to the task's complexity in an automated way.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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