{"paper":{"title":"Resolving Action Bottleneck: Agentic Reinforcement Learning Informed by Token-Level Energy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Token-level signals concentrate on action tokens in agentic RL, so reweighting gradients toward them outperforms uniform policy gradients.","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"David Wipf, Henry Peng Zou, Junhua Liu, Junyou Zhu, Langzhou He, Philip S. Yu, Qitian Wu, Wei-Chieh Huang, Yue Zhou, Zhengyao Gu","submitted_at":"2026-05-14T08:33:02Z","abstract_excerpt":"Agentic reinforcement learning trains large language models using multi-turn trajectories that interleave long reasoning traces with short environment-facing actions. Common policy-gradient methods, such as PPO and GRPO, treat each token in a trajectory equally, leading to uniform credit assignment. In this paper, we critically demonstrate that such uniform credit assignment largely misallocates token-level training signals. From an energy-based modeling perspective, we show that token-level training signals, quantified by their correlations with reward variance of different rollouts sampled f"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"token-level training signals, quantified by their correlations with reward variance of different rollouts sampled from a given prompt, concentrate sharply on action tokens rather than reasoning tokens, even though action tokens account for only a small fraction of the trajectory","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"that down-weighting reasoning tokens and boosting high-uncertainty action tokens will not degrade the quality of the reasoning chain itself or introduce new instabilities in long-horizon trajectories","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"ActFocus resolves the action bottleneck in agentic RL by reweighting token gradients toward action tokens using observed reward variance and an energy-based uncertainty term, outperforming PPO and GRPO by up to 65 percentage points.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Token-level signals concentrate on action tokens in agentic RL, so reweighting gradients toward them outperforms uniform policy gradients.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"89df5c0110227a37e42f43dd209e275d1d2e766ec454baf5a00e52c833d09f07"},"source":{"id":"2605.14558","kind":"arxiv","version":1},"verdict":{"id":"0fc00fb7-6634-456c-8f63-2e929df9641c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T01:43:12.935990Z","strongest_claim":"token-level training signals, quantified by their correlations with reward variance of different rollouts sampled from a given prompt, concentrate sharply on action tokens rather than reasoning tokens, even though action tokens account for only a small fraction of the trajectory","one_line_summary":"ActFocus resolves the action bottleneck in agentic RL by reweighting token gradients toward action tokens using observed reward variance and an energy-based uncertainty term, outperforming PPO and GRPO by up to 65 percentage points.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"that down-weighting reasoning tokens and boosting high-uncertainty action tokens will not degrade the quality of the reasoning chain itself or introduce new instabilities in long-horizon trajectories","pith_extraction_headline":"Token-level signals concentrate on action tokens in agentic RL, so reweighting gradients toward them outperforms uniform policy gradients."},"references":{"count":39,"sample":[{"doi":"","year":2023,"title":"Lmrl gym: Benchmarks for multi-turn reinforcement learn- ing with language models","work_id":"bad1baad-5c3f-4456-ac96-29c8f5e78bfb","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Chan, Hao Sun, Samuel Holt, and Mihaela van der Schaar","work_id":"c8748d0b-32ee-428e-a008-b6fee31a2bff","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"arXiv preprint arXiv:2502.01600 , year=","work_id":"7e929792-6a2a-42ff-a1db-763f890d8b4e","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Process reinforcement through implicit rewards,","work_id":"9ea5754d-d348-48ee-9421-97a9f4c6d3eb","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Process Reinforcement through Implicit Rewards","work_id":"c31a2126-86f9-44f3-91f3-208d0fc1463a","ref_index":5,"cited_arxiv_id":"2502.01456","is_internal_anchor":true}],"resolved_work":39,"snapshot_sha256":"8887903501c992eb3daf01e2640401d8063f10ac6db147c7fe14f36e48e00b83","internal_anchors":17},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2658e067e3a21f2e238b50c054966600406cf2f3e106331f0fd19623b5045d36"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}