{"paper":{"title":"KVPO: ODE-Native GRPO for Autoregressive Video Alignment via KV Semantic Exploration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"KVPO aligns streaming autoregressive video generators by routing semantic variations through the KV cache and modeling policies via velocity energy in ODE space.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jun Zhou, Kaixi Cong, Ruicheng Zhang, Shuiyang Mao, Wei Liu, Xiu Li, Zhizhou Zhong, Zunnan Xu","submitted_at":"2026-05-14T02:24:46Z","abstract_excerpt":"Aligning streaming autoregressive (AR) video generators with human preferences is challenging. Existing reinforcement learning methods predominantly rely on noise-based exploration and SDE-based surrogate policies that are mismatched to the deterministic ODE dynamics of distilled AR models, and tend to perturb low-level appearance rather than the high-level semantic storyline progression critical for long-horizon coherence. To address these limitations, we present KVPO, an ODE-native online Group Relative Policy Optimization (GRPO) framework for aligning streaming video generators. For diversi"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"KVPO introduces a causal-semantic exploration paradigm that relocates the source of variation from stochastic noise to the historical KV cache, constructing semantically diverse generation branches that remain strictly on the data manifold, and a velocity-field surrogate policy based on Trajectory Velocity Energy that yields a reward-weighted contrastive objective fully consistent with the native ODE formulation.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That stochastically routing historical KV entries produces semantically diverse generation branches that remain strictly on the data manifold without introducing artifacts or deviating from the model's learned distribution.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"KVPO aligns streaming autoregressive video generators with human preferences via ODE-native GRPO, using KV cache for semantic exploration and TVE for velocity-based policy modeling, yielding gains in quality and alignment.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"KVPO aligns streaming autoregressive video generators by routing semantic variations through the KV cache and modeling policies via velocity energy in ODE space.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3854c0bab6a8440eef79302105917c1f916ce464dae5b55e440e45335c16d341"},"source":{"id":"2605.14278","kind":"arxiv","version":1},"verdict":{"id":"f7558614-696f-4ed3-87a9-cce9d31f28a9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:35:53.482864Z","strongest_claim":"KVPO introduces a causal-semantic exploration paradigm that relocates the source of variation from stochastic noise to the historical KV cache, constructing semantically diverse generation branches that remain strictly on the data manifold, and a velocity-field surrogate policy based on Trajectory Velocity Energy that yields a reward-weighted contrastive objective fully consistent with the native ODE formulation.","one_line_summary":"KVPO aligns streaming autoregressive video generators with human preferences via ODE-native GRPO, using KV cache for semantic exploration and TVE for velocity-based policy modeling, yielding gains in quality and alignment.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That stochastically routing historical KV entries produces semantically diverse generation branches that remain strictly on the data manifold without introducing artifacts or deviating from the model's learned distribution.","pith_extraction_headline":"KVPO aligns streaming autoregressive video generators by routing semantic variations through the KV cache and modeling policies via velocity energy in ODE space."},"references":{"count":33,"sample":[{"doi":"","year":2025,"title":"arXiv preprint arXiv:2511.16955 (2025) 3","work_id":"20b57bf7-3f3a-4078-a74b-d8065c609465","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"arXiv preprint arXiv:2603.17461 (2026)","work_id":"20db7b14-851b-4a3b-9b8a-53fe5fca0290","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"arXiv preprint arXiv:2603.21299 (2026)","work_id":"d62c2c5a-3956-4522-bb31-438b3c66c6c5","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"LoRA: Low-Rank Adaptation of Large Language Models","work_id":"0426219a-789e-4964-adc8-a04538510818","ref_index":4,"cited_arxiv_id":"2106.09685","is_internal_anchor":true},{"doi":"","year":2025,"title":"Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion","work_id":"53e58ef9-7932-4b83-b757-34ac14db3e0f","ref_index":5,"cited_arxiv_id":"2506.08009","is_internal_anchor":true}],"resolved_work":33,"snapshot_sha256":"536eea658fb28bb4fd023b78b136fe998ec051f75f2dc0fa7045ea4cff6b43d6","internal_anchors":12},"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"}