Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 9 of its 25 references that are in the map. First posted on arXiv 21 May 2026.
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- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 886
- 2024Large Language Models for Mathematical Reasoning: Progresses and Challengescited by 284
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- 2025Reward Evolution with Graph-of-Thoughts: A Bi-Level Language Model Framework for Reinforcement Learning
- 2024GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning
- 2025RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning
- 2026CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
- 2025Adaptive Graph of Thoughts: Test-Time Adaptive Reasoning Unifying Chain, Tree, and Graph Structures
- 2025G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
- 2023Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
- 2024Reinforcement Learning Problem Solving with Large Language Models