PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks
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 agenti...
| Authors | Yuyao Zheng, Haipeng Sun, Junwei Bao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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