Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning. A single checkpoint that serves both would defer this choice to inference, when deployment constraints (rollout cost, observation accessibility) determine which path wins.
Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future embeddings, but the objective admits a trivial solution of a constant encoder, so every practical system adds an anti-collapse mechanism (LeCun, 2022; Assran et al. , 2023; Bardes et al.
arXiv:2608. 17542v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future embeddings, but the objective admits a trivial solution of a constant encoder, so every practical system adds an anti-collapse mechanism (LeCun, 2022; Assran et al.
By Jack Boylan, Chris Hokamp
arXiv:2607. 05238v1 Announce Type: new Abstract: JEPA world models predict the next latent state with a single deterministic predictor trained by latent regression.
By Zhi Song, Ximing Xing, Zhenchao Tang, hanbo Huang, Tianxu Lv, minghao Yang, Zhongzheng Niu, He Bing, Lusheng Wang, Jianhua Yao
arXiv:2605. 08732v2 Announce Type: replace-cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging.
By Hoang Nguyen, Xiaohao Xu, Xiaonan Huang
arXiv:2608. 17163v1 Announce Type: cross Abstract: Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Language-Action models into reliable, high-performing policies.
By Perry Dong, Yueru Jia, Chelsea Finn, Dorsa Sadigh