Humans learn by observing, interacting with environments, and internalizing physics and causality. Here, we aim to ask whether an agent can similarly acquire human-like reasoning from interaction and keep improving with more experience. We study this in a Game-to-Unseen (G2U) setting, curating 1,000+ heterogeneous games with diverse physical and causal mechanisms, and evaluate at three human-like levels: Survival, Curiosity, Utility, from primitive intuition to goal-driven reasoning. Our analysis reveals complementary failures: VLM/VLA agents reason but lack look-ahead in interactive settings, while world models imagine but imitate visual patterns rather than analyze physics and causality. We therefore propose IPR (Interactive Physical Reasoner), using world-model rollouts to score and reinforce a VLM's policy, and introduce PhysCode, a physics-centric action code aligning semantic intent with dynamics to provide a shared action space for prediction and reasoning. Pretrained on 1,000+ games, our IPR performs robustly on three levels, matches GPT-5 overall, and surpasses it on Curiosity. We find that performance improves with more training games and interaction steps, and that the model also zero-shot transfers to unseen games. These results support physics-centric interaction as a path to steadily improving physical reasoning.

翻译：人类通过观察、与环境交互，并内化物理规律和因果关系进行学习。本文旨在探究智能体是否能够通过类似交互方式习得类人推理能力，并随着经验积累持续提升。我们在“游戏到未见”（G2U）设定下开展研究，构建了包含1,000余款具有多样化物理与因果机制的游戏环境，并从三个类人认知层次进行评估：生存、好奇心和实用性——涵盖从原始直觉到目标驱动的推理过程。分析揭示了互补性缺陷：视觉语言模型（VLM/VLA）虽能推理，但在交互场景中缺乏前瞻性；世界模型虽能想象，却仅模仿视觉模式而非分析物理与因果关系。为此，我们提出交互式物理推理器（IPR），利用世界模型推演来评估并强化视觉语言模型的策略，同时引入以物理为中心的动作编码（PhysCode），将语义意图与动力学对齐，为预测与推理提供共享动作空间。在1,000余款游戏上预训练后，我们的IPR在三个认知层次均表现稳健，整体性能与GPT-5相当，并在好奇心维度实现超越。研究发现，模型性能随训练游戏数量与交互步数增加而提升，且能零样本迁移至未见游戏。这些结果表明，以物理为中心的交互是实现物理推理能力持续提升的有效路径。