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Annotations as Rollouts: Efficient and Scalable Reinforcement Learning for Video MLLMs

98 upvotes · 20 AUG 2026 · Yunheng Li, Guohong Mu, Hao Li et al.

This paper introduces a method called OraRL to improve the efficiency and scalability of reinforcement learning for multimodal large language models (MLLMs) trained on video data. By leveraging annotations as a source of high-quality rollouts, OraRL can significantly reduce the number of required rollouts, leading to faster training times and better performance on video understanding tasks.

reinforcement learningmultimodal large language modelsvideo perceptionoracle rolloutschain-of-thought generation