ThinkWorld: World Imagination Reinforces Chain-of-Dynamics Reasoning
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arXiv:2610.37666v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have made substantial progress in robotic manipulation, but their generalization remains constrained by the diversity and coverage of action-labeled robot demonstrations. Human videos provide much broader behavioral and environmental experience, while learning from them faces a fundamental trade-off between scalable data and explicit action grounding. We introduce ThinkWorld, a VLA learning framework that grounds action reasoning in world dynamics and strengthens it through reinforcement learning with world imagination. ThinkWorld represents each action thought as a Chain-of-Dynamics (CoD), a sequence of discrete motion tokens that captures the intermediate dynamics connecting the current world to a future state and serves as a common action representation across embodiments. We first initialize CoD with action-labeled demonstrations, then predict the future induced by each CoD and reinforce those whose predictions better match the observed outcome. This enables videos without action annotations to provide direct supervision from observed state transitions, allowing human and robot experience to refine the same reasoning space. Experiments in simulation and on real robots demonstrate effective human-to-robot transfer and strong generalization to unseen tasks and environments.
Why It Matters
Vision-Language-Action models have advanced robotic manipulation, but generalisation across environments remains limited. ThinkWorld uses world imagination—mentally simulating dynamics—to reinforce chain-of-dynamics reasoning for robots. It matters for making robot policies that reason about consequences rather than reacting. See Agents, Autonomy, and Tool Use, World Models, and Multimodal and Generative Capabilities.
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