{"slug": "group-in-group-policy-optimization-for-llm-agent-training-interactive-visual", "title": "Group-in-Group Policy Optimization for LLM Agent Training — interactive visual explainer | Rudrite Research", "summary": "Feng et al. published Group-in-Group Policy Optimization for LLM Agent Training at NeurIPS 2025, introducing a method that provides step-level credit to long-horizon LLM agents without a critic. An interactive visual explainer of the paper is now available online.", "body_md": "# Group-in-Group Policy Optimization for LLM Agent Training\n\nGroup-in-group advantages give long-horizon LLM agents step-level credit without a critic.\n\nFeng et al. · NeurIPS 2025 · Reasoning & RL. [Read the paper ↗](https://arxiv.org/abs/2505.10978)\n\nA free, interactive, animated visual explainer of Group-in-Group Policy Optimization for LLM Agent Training — every exhibit computed from the real formulas, with verbatim quotes from the source.\n\n## Questions\n\n- What is Group-in-Group Policy Optimization for LLM Agent Training?\n- Group-in-group advantages give long-horizon LLM agents step-level credit without a critic.\n- Who published Group-in-Group Policy Optimization for LLM Agent Training, and where?\n- Feng et al. — NeurIPS 2025 (arXiv:2505.10978).\n- Where can I find a visual explainer of Group-in-Group Policy Optimization for LLM Agent Training?\n- Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source.\n\n## Related explainers\n\n[DeepSeek-R1](/deepseek-r1)[Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](/chain-of-thought)[Training language models to follow instructions with human feedback](/instructgpt)[Direct Preference Optimization: Your Language Model is Secretly a Reward Model](/dpo)[DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](/deepseekmath)[Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](/test-time-compute)[Constitutional AI: Harmlessness from AI Feedback](/constitutional-ai)[DAPO: An Open-Source LLM Reinforcement Learning System at Scale](/dapo)", "url": "https://wpnews.pro/news/group-in-group-policy-optimization-for-llm-agent-training-interactive-visual", "canonical_source": "https://research.rudrite.com/gigpo", "published_at": "2026-06-13 00:00:00+00:00", "updated_at": "2026-06-14 18:18:02.985536+00:00", "lang": "en", "topics": ["large-language-models", "ai-research"], "entities": ["Feng", "NeurIPS 2025", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/group-in-group-policy-optimization-for-llm-agent-training-interactive-visual", "markdown": "https://wpnews.pro/news/group-in-group-policy-optimization-for-llm-agent-training-interactive-visual.md", "text": "https://wpnews.pro/news/group-in-group-policy-optimization-for-llm-agent-training-interactive-visual.txt", "jsonld": "https://wpnews.pro/news/group-in-group-policy-optimization-for-llm-agent-training-interactive-visual.jsonld"}}