Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs

• URL: http://arxiv.org/abs/2512.17008v1
• Date: Thu, 18 Dec 2025 19:07:25 GMT
• Title: Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs
• Authors: Junbo Li, Peng Zhou, Rui Meng, Meet P. Vadera, Lihong Li, Yang Li,
• Abstract summary: We investigate more stable and effective advantage estimation strategies, especially for multi-turn settings.<n>We first explore Proximal Policy Optimization (PPO) as an alternative and find it to be more robust than GRPO.<n>To further enhance PPO in multi-turn scenarios, we introduce turn-PPO, a variant that operates on a turn-level MDP formulation.
• Score: 18.31183900162479
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments. However, directly applying the widely used Group Relative Policy Optimization (GRPO) algorithm to multi-turn tasks exposes notable limitations, particularly in scenarios requiring long-horizon reasoning. To address these challenges, we investigate more stable and effective advantage estimation strategies, especially for multi-turn settings. We first explore Proximal Policy Optimization (PPO) as an alternative and find it to be more robust than GRPO. To further enhance PPO in multi-turn scenarios, we introduce turn-PPO, a variant that operates on a turn-level MDP formulation, as opposed to the commonly used token-level MDP. Our results on the WebShop and Sokoban datasets demonstrate the effectiveness of turn-PPO, both with and without long reasoning components.

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