Looped World Models
Summary
Looped World Models introduce iterative latent state refinement through shared transformer blocks, achieving 100x parameter efficiency while adapting computational depth to prediction complexity.
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Source: https://huggingface.co/papers/2606.18208 Published on Jun 16
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Submitted byhttps://huggingface.co/hongyyyyy
luon Jun 17
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Abstract
Looped World Models introduce iterative latent state refinement through shared transformer blocks, achieving 100x parameter efficiency while adapting computational depth to prediction complexity.
Currentworld modelsface a fundamental tension: faithful long-horizon simulation demands deep computation, but deeper models are expensive to deploy and prone to compounding errors. We resolve this by introducing LoopedWorld Models(LoopWM), which are the firstlooped architecturesfor world modelling. Our method iteratively refineslatent environment statesthrough aparameter-shared transformer block. This yield up to 100x parameter efficiency over conventional approaches withadaptive computationthat automatically scales depth to match the complexity of each prediction step. Orthogonal to scaling model size and training data, LoopWM establishes iterative latent depth as a new scaling axis for world simulation, which might significantly push the community forward.
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