Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment

• URL: http://arxiv.org/abs/2505.11821v1
• Date: Sat, 17 May 2025 04:09:46 GMT
• Title: Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment
• Authors: Siliang Zeng, Quan Wei, William Brown, Oana Frunza, Yuriy Nevmyvaka, Mingyi Hong,
• Abstract summary: This paper investigates approaches to enhance the reasoning capabilities of Large Language Model (LLM) agents using Reinforcement Learning (RL)<n>We introduce a fine-grained turn-level advantage estimation strategy to enable more precise credit assignment in multi-turn agent interactions.<n>Our method achieves 100% success in tool execution and 50% accuracy in exact answer matching, significantly outperforming baselines.
• Score: 29.617927643991877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates approaches to enhance the reasoning capabilities of Large Language Model (LLM) agents using Reinforcement Learning (RL). Specifically, we focus on multi-turn tool-use scenarios, which can be naturally modeled as Markov Decision Processes (MDPs). While existing approaches often train multi-turn LLM agents with trajectory-level advantage estimation in bandit settings, they struggle with turn-level credit assignment across multiple decision steps, limiting their performance on multi-turn reasoning tasks. To address this, we introduce a fine-grained turn-level advantage estimation strategy to enable more precise credit assignment in multi-turn agent interactions. The strategy is general and can be incorporated into various RL algorithms such as Group Relative Preference Optimization (GRPO). Our experimental evaluation on multi-turn reasoning and search-based tool-use tasks with GRPO implementations highlights the effectiveness of the MDP framework and the turn-level credit assignment in advancing the multi-turn reasoning capabilities of LLM agents in complex decision-making settings. Our method achieves 100% success in tool execution and 50% accuracy in exact answer matching, significantly outperforming baselines, which fail to invoke tools and achieve only 20-30% exact match accuracy.

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