{"paper":{"title":"GAGPO: Generalized Advantage Grouped Policy Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"GAGPO constructs a non-parametric grouped value proxy from sampled rollouts to compute temporal advantages for critic-free policy optimization in multi-turn environments.","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Chao Yu, Jinjun Hu, Qiwen Chen, Rongxin Yang, Siyuan Zhu, Yibo Zhang, Zongkai Liu","submitted_at":"2026-05-13T09:10:03Z","abstract_excerpt":"Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments remains a challenge. Agents often receive sparse, trajectory-level rewards only at the end of an episode, making it difficult to determine which intermediate actions contributed to success or failure. As a result, propagating delayed outcomes back to individual decision steps without relying on costly auxiliary value models remains an open problem. We propose Generalized Advantage Grouped Policy Optimization (GAGPO), a critic-free reinforcement l"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"GAGPO constructs a non-parametric grouped value proxy from sampled rollouts and uses it to compute TD/GAE-style temporal advantages, recursively propagating outcome supervision backward through time.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the non-parametric grouped value proxy derived from sampled rollouts provides sufficiently low-bias and low-variance estimates of true advantages to support stable policy updates without introducing systematic errors from grouping or sampling choices.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"GAGPO computes step-aligned temporal advantages from grouped rollout samples without a learned critic, enabling stable policy optimization in multi-turn agent environments.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"GAGPO constructs a non-parametric grouped value proxy from sampled rollouts to compute temporal advantages for critic-free policy optimization in multi-turn environments.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"87dc1dd425da78478a61b643ada6d2ceefd08dfa988f98f01a00405b414918ca"},"source":{"id":"2605.13217","kind":"arxiv","version":1},"verdict":{"id":"dcfe022f-a8a4-4dc4-860e-f0559b0f4d8a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T19:13:32.766124Z","strongest_claim":"GAGPO constructs a non-parametric grouped value proxy from sampled rollouts and uses it to compute TD/GAE-style temporal advantages, recursively propagating outcome supervision backward through time.","one_line_summary":"GAGPO computes step-aligned temporal advantages from grouped rollout samples without a learned critic, enabling stable policy optimization in multi-turn agent environments.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the non-parametric grouped value proxy derived from sampled rollouts provides sufficiently low-bias and low-variance estimates of true advantages to support stable policy updates without introducing systematic errors from grouping or sampling choices.","pith_extraction_headline":"GAGPO constructs a non-parametric grouped value proxy from sampled rollouts to compute temporal advantages for critic-free policy optimization in multi-turn environments."},"references":{"count":32,"sample":[{"doi":"","year":2022,"title":"Training language models to follow instructions with human feedback , author=. 2022 , eprint=","work_id":"5903b651-f252-45b3-9845-98a1abf380ae","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"OpenAI GPT-5 System Card , author=. 2025 , eprint=","work_id":"92a933bb-6195-449a-8230-f8407d9b8afc","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities , author=. 2025 , eprint=","work_id":"8fb5d482-cf5e-4141-b15a-3f607aa09f83","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Qwen3 Technical Report , author=. 2025 , eprint=","work_id":"26c7b6ed-f86e-4ed8-b9ed-b1783d90255b","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Proximal Policy Optimization Algorithms , author=. 2017 , eprint=","work_id":"5a794cfe-c0fb-4191-a347-b4e935d39d00","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":32,"snapshot_sha256":"7ef6813ede6c6d38e211834aa920c77a2b9914485b3c23dc16873a708f730b75","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}