H-JEPA

Headlines · 2

1. 5d
   LeCun's H-JEPA hierarchical world model takes aim at agents and RSI

   Yann LeCun's new world model H-JEPA is presented as the first reproducible implementation of the hierarchical JEPA idea from his 2022 paper: multiple JEPAs are stacked, each with its own observation encoder, action encoder and latent predictor, forecasting states at different time horizons, with high levels setting coarse goals that lower levels refine into actions. The article reports that a three-layer H-JEPA raised success on Visual AntMaze to 73% from 18% for single-layer LeWM, but on Push-T two layers worked best and three or four layers degraded, using roughly 58% and 14% of LeWM's transferable training data respectively; training uses neither reward nor reconstruction, and DROID teleoperation experiments needed an inverse-dynamics constraint to avoid slow-feature collapse. It frames H-JEPA as a counterpoint to today's agents, which split tasks into more steps and more tokens, and to recursive self-improvement, which LeCun calls only an amplifier of existing abilities.

   虎嗅 AI · 🔥 0
2. 7d
   LeCun's AMI open-sources H-JEPA, a hierarchical world model for visual planning

   AMI (Advanced Machine Intelligence), the world-model startup chaired by Yann LeCun, released a paper with NYU, INRIA Paris and Brown University describing H-JEPA: multiple JEPA modules stacked so each layer predicts states at its own timescale, with a high level setting a coarse plan and lower levels refining predicted intermediate states into executable actions. The paper reports 73% success for a three-layer model versus 18% for a single-layer model on the Visual AntMaze simulated maze task, with better results at lower planning compute on several tasks; code and pretrained weights were released. The report also says AMI previously raised $1.03B at a $3.5B pre-money valuation, while its website still shows mainly research direction and hiring rather than a public product.

   36氪 人工智能 · 🔥 0

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