Astrolabe: Steering Forward-Process Reinforcement Learning for Distilled Autoregressive Video Models

TL;DR

Astrolabe introduces an efficient online reinforcement learning framework tailored for distilled autoregressive video models, improving alignment with human preferences without extensive re-distillation or high computational costs.

Contribution

The paper presents a novel forward-process RL formulation with negative-aware fine-tuning and a streaming training scheme for long videos, enabling scalable and effective alignment of AR video models.

Findings

Consistently improves generation quality across multiple models

Reduces computational overhead compared to existing methods

Enhances long-range coherence in video generation

Abstract

Distilled autoregressive (AR) video models enable efficient streaming generation but frequently misalign with human visual preferences. Existing reinforcement learning (RL) frameworks are not naturally suited to these architectures, typically requiring either expensive re-distillation or solver-coupled reverse-process optimization that introduces considerable memory and computational overhead. We present Astrolabe, an efficient online RL framework tailored for distilled AR models. To overcome existing bottlenecks, we introduce a forward-process RL formulation based on negative-aware fine-tuning. By contrasting positive and negative samples directly at inference endpoints, this approach establishes an implicit policy improvement direction without requiring reverse-process unrolling. To scale this alignment to long videos, we propose a streaming training scheme that generates sequences progressively via a rolling KV-cache, applying RL updates exclusively to local clip windows while conditioning on prior context to ensure long-range coherence. Finally, to mitigate reward hacking, we integrate a multi-reward objective stabilized by uncertainty-aware selective regularization and dynamic reference updates. Extensive experiments demonstrate that our method consistently enhances generation quality across multiple distilled AR video models, serving as a robust and scalable alignment solution.