{"paper":{"title":"StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"LLM agents need step-level MDP and credit assignment rather than token-level modeling for multi-turn RL.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daoyu Wang, Enhong Chen, Jie Ouyang, Mingyue Cheng, Qi Liu, Qingchuan Li, Shuo Yu","submitted_at":"2026-04-20T15:22:39Z","abstract_excerpt":"Agentic reinforcement learning (RL) is emerging as a critical post-training paradigm for improving LLM agent capabilities. Existing RL algorithms for LLMs largely follow the token-centric paradigm as in RLHF and RLVR, where tokens serve as the basic units for modeling and optimization. However, this paradigm introduces a granularity mismatch in agentic RL, as it optimizes token-level predictions while LLM agents make step-level decisions through cycles of environmental observations and actions. To bridge this gap, we propose \\textbf{StepPO}, a step-centric paradigm for agentic RL via step-alig"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"the conventional token-level Markov Decision Process (MDP) should be advanced to a step-level MDP formulation, and that the step, rather than the token, should be regarded as the proper action representation for LLM agents","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That redefining the MDP and credit assignment at step granularity will meaningfully address delayed/sparse rewards and long context challenges in multi-turn agent settings.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"StepPO argues that LLM agents should optimize at the step level rather than token level to better handle delayed rewards and long contexts in agentic RL.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"LLM agents need step-level MDP and credit assignment rather than token-level modeling for multi-turn RL.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a13be7d3ed31acb1022e48c7c1388a19ea717b0a6749fc0fe81cb0f2cbaefa1d"},"source":{"id":"2604.18401","kind":"arxiv","version":2},"verdict":{"id":"9f7497bf-1d29-4123-a5db-df28933e1a61","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T04:23:57.354913Z","strongest_claim":"the conventional token-level Markov Decision Process (MDP) should be advanced to a step-level MDP formulation, and that the step, rather than the token, should be regarded as the proper action representation for LLM agents","one_line_summary":"StepPO argues that LLM agents should optimize at the step level rather than token level to better handle delayed rewards and long contexts in agentic RL.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That redefining the MDP and credit assignment at step granularity will meaningfully address delayed/sparse rewards and long context challenges in multi-turn agent settings.","pith_extraction_headline":"LLM agents need step-level MDP and credit assignment rather than token-level modeling for multi-turn RL."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.18401/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-20T04:02:34.505902Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"e916b5c1865fb0d190821c203dfeac2c3e81eb7a3f5b3bc71143c18d8b737b04"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","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"}