GAGPO: Generalized Advantage Grouped Policy Optimization

• URL: http://arxiv.org/abs/2605.13217v1
• Date: Wed, 13 May 2026 09:10:03 GMT
• Title: GAGPO: Generalized Advantage Grouped Policy Optimization
• Abstract summary: We propose a critic-free reinforcement learning method for precise, step-aligned temporal credit assignment.<n>Experiments on ALFWorld and WebShop show that GAGPO outperforms strong reinforcement learning baselines.
• Score: 13.085463559931156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments remains a challenge. Agents often receive sparse, trajectory-level rewards only at the end of an episode, making it difficult to determine which intermediate actions contributed to success or failure. As a result, propagating delayed outcomes back to individual decision steps without relying on costly auxiliary value models remains an open problem. We propose Generalized Advantage Grouped Policy Optimization (GAGPO), a critic-free reinforcement learning method for precise, step-aligned temporal credit assignment. GAGPO constructs a non-parametric grouped value proxy from sampled rollouts and uses it to compute TD/GAE-style temporal advantages, recursively propagating outcome supervision backward through time. Combined with group-wise advantage normalization and an action-level importance ratio, GAGPO extracts stable, localized optimization signals directly from multi-turn trajectories. Experiments on ALFWorld and WebShop show that GAGPO outperforms strong reinforcement learning baselines. Further analyses demonstrate faster early-stage learning, improved interaction efficiency, and smoother optimization dynamics, suggesting that GAGPO offers a simple yet effective framework for multi-turn agentic reinforcement learning.

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