WAM-RL: 再構成報酬とオンラインビデオSFTを用いたワールド・アクションモデル強化学習
WAM-RL: World-Action Model Reinforcement Learning with Reconstruction Rewards and Online Video SFT
ワールドモデルとアクションモデルをオンライン環境相互作用で共同最適化する強化学習フレームワークを提案し、長期的タスクでの性能向上を実証した。
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著者: Zezhong Qian, Xiaowei Chi, Yu Qi, Haozhan Li, Zhi Yang Chen, Shanghang Zhang
分類: cs.RO
原文アブストラクト
Recent World-Action (WA) models demonstrate strong generalization ability and data efficiency, but they typically rely on expert trajectories for training. This reliance limits their ability to acquire fine-grained manipulation skills beyond the demonstration distribution and prevents them from continuously improving through real-world interaction. To address these limitations, we propose WAM-RL, a reinforcement learning framework that enables joint optimization of the world model and the action model through online interaction with the environment. By allowing the two components to co-evolve, our approach enhances fine-grained control and adaptability. Specifically, a WA model consists of a world model and an actor. We design a tailored reinforcement learning method with hierarchical optimization to coordinate their improvement. On the methodological side, we systematically investigate the effects of applying reinforcement learning to the action model, as well as online training of the world model within an RL setting. Our experiments reveal a key insight: optimizing only the actor yields improvements on short-horizon tasks, but fails to provide significant gains on long-horizon tasks. In contrast, jointly optimizing both the world model and the actor is critical for achieving strong performance in long-horizon settings. Our work is the first to introduce reinforcement learning into the World-Action paradigm, and provides insights into how online optimization of both the action head and the world model impacts overall performance.
