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.

Related

• Progress- and Reliability-Oriented Group Policy Optimization for Agentic Reinforcement Learning
• PlanPO: Group Planning-Aware Policy Optimization for Multi-Turn Agentic LLMs
• Spend Your Rollouts Where It Counts: Rollout Allocation for Group-Based RL Post-Training
• GAGPO: Generalized Advantage Grouped Policy Optimization
• Progress-conditioned Group Policy Optimization for Long-Horizon Agentic Tasks

Source: arXiv cs.AI | 2026-09-03