AR-CoPO: Align Autoregressive Video Generation with Contrastive Policy Optimization

• URL: http://arxiv.org/abs/2603.17461v1
• Date: Wed, 18 Mar 2026 08:07:01 GMT
• Title: AR-CoPO: Align Autoregressive Video Generation with Contrastive Policy Optimization
• Abstract summary: Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis.<n>We propose AR-CoPO, a framework that adapts the Neighbor GRPO contrastive perspective to streaming AR generation.
• Score: 47.39648302477585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streaming autoregressive (AR) video generators combined with few-step distillation achieve low-latency, high-quality synthesis, yet remain difficult to align via reinforcement learning from human feedback (RLHF). Existing SDE-based GRPO methods face challenges in this setting: few-step ODEs and consistency model samplers deviate from standard flow-matching ODEs, and their short, low-stochasticity trajectories are highly sensitive to initialization noise, rendering intermediate SDE exploration ineffective. We propose AR-CoPO (AutoRegressive Contrastive Policy Optimization), a framework that adapts the Neighbor GRPO contrastive perspective to streaming AR generation. AR-CoPO introduces chunk-level alignment via a forking mechanism that constructs neighborhood candidates at a randomly selected chunk, assigns sequence-level rewards, and performs localized GRPO updates. We further propose a semi-on-policy training strategy that complements on-policy exploration with exploitation over a replay buffer of reference rollouts, improving generation quality across domains. Experiments on Self-Forcing demonstrate that AR-CoPO improves both out-of-domain generalization and in-domain human preference alignment over the baseline, providing evidence of genuine alignment rather than reward hacking.

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