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Temporal-Distance JEPA enhances world model predictive control

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced Temporal-Distance JEPA (TD-JEPA), a novel approach to representation learning for latent world model predictive control. This method enhances Joint-Embedding Predictive Architectures (JEPAs) by mining a directed temporal cost from reward-free trajectories, improving the ability to rank imagined futures by goal progress. TD-JEPA demonstrates significant performance gains in environments like Two-Room and OGB-Cube, outperforming previous methods by effectively bridging the gap between training and planning. AI

IMPACT Improves planning capabilities in AI agents by better understanding temporal progress in learned world models.

RANK_REASON The cluster contains a research paper detailing a new method for representation learning in AI.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Temporal-Distance JEPA enhances world model predictive control

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The cluster contains a research paper detailing a new method for representation learning in AI.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Jiaxin Bai, Jiaxuan Xiong ·

    Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control

    arXiv:2607.25337v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting in representation space rather than reconstructing pixels, making them a natural backbone for latent model predictive control from offline demonstrati…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control

    Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting in representation space rather than reconstructing pixels, making them a natural backbone for latent model predictive control from offline demonstration logs. JEPA-style training optimizes short-hor…

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