Dwell 框架：让视觉语言模型在潜空间中通过想象自我改进
Vision-Language Agents Can Learn in Their Own Latent Worlds
把 VLM 当成世界模型和策略，在潜空间里自己想象自己练，五个环境里成绩最好还省算力，做智能体的可以看看。
论文提出 Dwell，一个基于模型的强化学习框架，将 VLM 重新定义为循环潜空间状态转移算子。VLM 同时担任世界模型和策略两个角色，通过真实交互预测动作结果，并在潜空间想象中改进行为。在五个交互式视觉环境中，Dwell 取得可训练方法中最强的整体性能，同时大幅降低了想象所需的计算量。代码已在 GitHub 公开。
Vision-Language Agents Can Learn in Their Own Latent Worlds
Learning from experience is essential for building the next generation of agents that continually expand their capabilities. Pretrained vision-language models (VLMs) already internalize rich world knowledge, yet this knowledge is rarely leveraged to drive policy improvement through online interaction. Realizing this potential efficiently remains challenging, as policy updates shift the agent's interaction distribution, requiring the world model to adapt accordingly, while continually updating large pretrained models is computationally expensive. To address this challenge, we introduce Dwell, a model-based reinforcement learning (RL) framework that reframes a VLM as a recurrent latent transition operator, endowing it with the ability to predict, adjust, and act in latent space. Leveraging the VLM's own representations and its dual role as both world model and policy, the agent continually learns from real interactions to anticipate and evaluate action outcomes, and from latent imagination to improve its behavior. Across five interactive visual environments, Dwell achieves the strongest overall performance among trainable methods while substantially reducing the compute required for imagination, showing that effective world models for VLM agents need not reconstruct the future; they need to predict futures that are useful for action. Our code is publicly available at https://github.com/Tom111-lhx/Dwell