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Group-in-Group Policy Optimization for LLM Agent Training

Paper · 2025 · Lang Feng, Zhenghai Xue, Tingcong Liu et al.

Summary

Group-based RL methods such as GRPO work well for single-turn LLM tasks like math. They struggle with multi-turn agents, where episodes can run for dozens of steps and rewards often come only at the end, so individual actions are hard to credit. The authors propose GiGPO (Group-in-Group Policy Optimization), which keeps GRPO's critic-free, memory-light design and adds a second, finer level of advantage estimation. At the trajectory level, it compares whole rollouts of the same task, as GRPO does. At the step level, it reuses the same rollouts: it finds environment states that recur across trajectories ("anchor states") and compares the actions taken from each such state. This needs no extra sampling and no value model. With Qwen2.5-1.5B/3B/7B-Instruct, GiGPO outperforms GRPO, prompt-based agents and actor-critic baselines on ALFWorld, WebShop and search-augmented QA. GPU memory and rollout cost stay the same. The result matters because it offers a cheap way to bring step-level credit assignment into scalable agent RL.

Key points

  • Results: gains over GRPO exceed 12% on ALFWorld and 9% on WebShop. On search-augmented QA, GiGPO reaches 42.1% with the 3B model and 47.2% with the 7B model.
  • Overhead: each training iteration takes about 362.83 s for rollouts, probability computation and updates. Anchor grouping adds only 0.01 s and step-advantage computation adds 0.53 s, a negligible share.
  • Ablations: removing either the episode-level or the step-level advantage clearly hurts performance. Dropping the step-level advantage hurts most on harder tasks such as Cool, Pick2 and WebShop.
  • Group dynamics: on ALFWorld, more than 65% of states recur across trajectories. As training converges, step-group sizes cluster around 6–8 (with a group size of N=8) and success rises above 80%.
  • Limitation and resources: the method depends on detecting identical states; when no states repeat, it falls back to plain GRPO. The paper appeared at NeurIPS 2025, and code is released as verl-agent.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Nanyang Technological UniversitySingapore · Singapore184 works · 27,484 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

Cite

Cite as Lang Feng, Zhenghai Xue, Tingcong Liu and Bo An. 2025. Group-in-Group Policy Optimization for LLM Agent Training. arXiv:2505.10978.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2505.10978 · arXiv 2505.10978

@misc{feng2025group,
  title = {{Group-in-Group Policy Optimization for LLM Agent Training}},
  author = {Lang Feng, Zhenghai Xue, Tingcong Liu and Bo An},
  year = {2025},
  doi = {10.48550/arxiv.2505.10978},
  eprint = {2505.10978},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.10978}
}

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