JEDI is the first online end-to-end latent diffusion world model that trains latents from denoising loss rather than reconstruction, achieving competitive Atari100k results with 43% less VRAM and over 3x faster sampling than pixel diffusion baselines.
Mastering atari, go, chess and shogi by planning with a learned model.Nature, 588(7839):604–609
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LLMs exhibit myopic planning in games, with move choices driven by shallow nodes despite deep reasoning traces, in contrast to human deep-search reliance.
RC-aux corrects spatiotemporal mismatch in reconstruction-free latent world models by adding multi-horizon prediction and reachability supervision, improving planning performance on goal-conditioned pixel-control tasks.
citing papers explorer
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JEDI: Joint Embedding Diffusion World Model for Online Model-Based Reinforcement Learning
JEDI is the first online end-to-end latent diffusion world model that trains latents from denoising loss rather than reconstruction, achieving competitive Atari100k results with 43% less VRAM and over 3x faster sampling than pixel diffusion baselines.
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Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning
LLMs exhibit myopic planning in games, with move choices driven by shallow nodes despite deep reasoning traces, in contrast to human deep-search reliance.
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Predictive but Not Plannable: RC-aux for Latent World Models
RC-aux corrects spatiotemporal mismatch in reconstruction-free latent world models by adding multi-horizon prediction and reachability supervision, improving planning performance on goal-conditioned pixel-control tasks.