JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts

Abstract:World models enable agents to plan by predicting future states of the environment, but their predictions can become unreliable when test-time dynamics differ from those seen during training. We present JEPA-TTT, which adapts the latent dynamics predictor of a pretrained action-conditioned Joint-Embedding Predictive Architecture world model throughout test time. Self-supervised updates accumulate across episodes, while the visual encoder and reward head remain fixed, preserving the pretrained representation and task objective. Planning requires neither a goal image nor online environment reward. JEPA-TTT uses dense replay, which forms prediction windows at every temporal offset, retains them in a growing buffer, and samples minibatches from that buffer for predictor updates. Across eight dynamics shifts in four continuous-control environments, JEPA-TTT improves planning on every shift. After 500 test-time episodes, it reduces autoregressive latent prediction error by 83% on average and improves planning performance by 153% over the frozen JEPA world model. These results show that persistent self-supervised test-time training can adapt a pretrained latent world model under changed dynamics.

Machine Learning (cs.LG)

arXiv:2610.00722 [cs.LG]

(or arXiv:2610.00722v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.00722 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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