Progress- and Reliability-Oriented Group Policy Optimization for Agentic Reinforcement Learning
Group-based reinforcement learning (RL) has become an effective paradigm for improving large language model agents on long-horizon interactive tasks. To obtain finer-grained policy updates than trajectory-level optimization, recent work has moved toward step-level group-based RL, where intermediate steps are grouped and compared within a rollout batch. However, step-level advantage estimation is sensitive to how groups are formed: grouping by broad state keys improves coverage but may compare actions taken under different histories, while enforcing historical consistency yields fairer comparisons at the cost of fragmented groups and missing peer-comparison signal. In this paper, we propos...
| Authors | Mingxuan Fan, Peiyang Liu |
|---|---|
| Primary category | cs.AI |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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