aiqbench analysis
This research introduces Potential-Guided Policy Optimization (PGPO) to address coarse credit assignment in multi-turn agentic tasks, particularly within failed trajectories where traditional methods struggle to differentiate effective actions. By leveraging empirical state potentials derived from group return statistics, PGPO enables cross-trajectory credit propagation that provides finer-grained step-level signals without significant training overhead. Experimental results on ALFWorld and WebShop demonstrate that this approach outperforms recent group-based reinforcement learning methods in handling sparse rewards.
PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks
arXiv:2609.02236v1 Announce Type: new Abstract: Group-based reinforcement learning (RL) has become an effective paradigm for LLM post-training, but in multi-turn agentic tasks with sparse terminal rewards, it often provides coarse credit for intermediate actions. To obtain more fine-grained credit assignment, recent work such as GiGPO introduces step-level advantages for intermediate actions. However, these step-level signals still rely on the final outcome of each individual trajectory. As a result, actions within failed trajectories can remain poorly differentiated, so effective actions can receive the same unfavorable credit as erroneous ones. In this work, we propose Potential-Guided Policy Optimization (PGPO) for multi-turn agentic tasks. PGPO estimates empirical state potentials from anchor-state-group return statistics within each rollout group. It then derives action advantages from potential differences between adjacent states, enabling cross-trajectory credit propagation. This provides finer-grained step-level credit assignment, especially within failed trajectories. Experiments on ALFWorld and WebShop show strong overall performance relative to recent group-based RL methods. Further analysis provides evidence that PGPO yields more informative failure-side credit signals with negligible training overhead.
Abstract:Group-based reinforcement learning (RL) has become an effective paradigm for LLM post-training, but in multi-turn agentic tasks with sparse terminal rewards, it often provides coarse credit for intermediate actions. To obtain more fine-grained credit assignment, recent work such as GiGPO introduces step-level advantages for intermediate actions. However, these step-level signals still rely on the final outcome of each individual trajectory. As a result, actions within failed trajectories can remain poorly differentiated, so effective actions can receive the same unfavorable credit as erroneous ones. In this work, we propose Potential-Guided Policy Optimization (PGPO) for multi-turn agentic tasks. PGPO estimates empirical state potentials from anchor-state-group return statistics within each rollout group. It then derives action advantages from potential differences between adjacent states, enabling cross-trajectory credit propagation. This provides finer-grained step-level credit assignment, especially within failed trajectories. Experiments on ALFWorld and WebShop show strong overall performance relative to recent group-based RL methods. Further analysis provides evidence that PGPO yields more informative failure-side credit signals with negligible training overhead.
Artificial Intelligence (cs.AI)
arXiv:2609.02236 [cs.AI]
(or arXiv:2609.02236v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.02236 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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