WorldCache: Accelerating World Models for Free via Heterogeneous Token Caching
Diffusion-based world models have shown strong potential for unified world simulation, but the iterative denoising remains too costly for interactive use and long-horizon rollouts. While feature caching can accelerate inference without training, we find that policies designed for single-modal diffusion transfer poorly to world models due to two world-model-specific obstacles: token heterogeneity from multi-modal coupling and spatial variation, and non-uniform temporal dynamics where a small set of hard tokens drives error growth, making uniform skipping either unstable or overly conservative. We propose WorldCache, a caching framework tailored to diffusion world models. We introduce Curvature-guided Heterogeneous Token Prediction, which uses a physics-grounded curvature score to estimate token predictability and applies a Hermite-guided damped predictor for chaotic tokens with abrupt direction changes. We also design Chaotic-prioritized Adaptive Skipping, which accumulates a curvature-normalized, dimensionless drift signal and recomputes only when bottleneck tokens begin to drift. Experiments on diffusion world models show that WorldCache delivers up to 3.7times end-to-end speedups while maintaining 98\% rollout quality, demonstrating the vast advantages and practicality of WorldCache in resource-constrained scenarios. Our code is released in https://github.com/FofGofx/WorldCache.
69aea0a69893f58272f742aa
Weilun Feng
claimed_verified
2026-03-09T10:37:53.499Z
68d63e44ccf464a96ac18bcb
/avatars/01b93c353a7231fa9e053ba9edcb2b06.svg
Weilun Feng
wlfeng
user
wlfeng
69aea0a69893f58272f742ab
Guoxin Fan
69aea0a69893f58272f742ac
Haotong Qin
69aea0a69893f58272f742ad
Chuanguang Yang
69aea0a69893f58272f742ae
Mingqiang Wu
69aea0a69893f58272f742af
Yuqi Li
69aea0a69893f58272f742b0
Xiangqi Li
69aea0a69893f58272f742b1
Zhulin An
69aea0a69893f58272f742b2
Libo Huang
69aea0a69893f58272f742b3
Dingrui Wang
69aea0a69893f58272f742b4
Longlong Liao
69aea0a69893f58272f742b5
Michele Magno
69aea0a69893f58272f742b6
Yongjun Xu
WorldCache: Accelerating World Models for Free via Heterogeneous Token Caching
Diffusion-based world models have shown strong potential for unified world simulation, but the iterative denoising remains too costly for interactive use and long-horizon rollouts. While feature caching can accelerate inference without training, we find that policies designed for single-modal diffusion transfer poorly to world models due to two world-model-specific obstacles: token heterogeneity from multi-modal coupling and spatial variation, and non-uniform temporal dynamics where a small set of hard tokens drives error growth, making uniform skipping either unstable or overly conservative. We propose WorldCache, a caching framework tailored to diffusion world models. We introduce Curvature-guided Heterogeneous Token Prediction, which uses a physics-grounded curvature score to estimate token predictability and applies a Hermite-guided damped predictor for chaotic tokens with abrupt direction changes. We also design Chaotic-prioritized Adaptive Skipping, which accumulates a curvature-normalized, dimensionless drift signal and recomputes only when bottleneck tokens begin to drift. Experiments on diffusion world models show that WorldCache delivers up to 3.7times end-to-end speedups while maintaining 98\% rollout quality, demonstrating the vast advantages and practicality of WorldCache in resource-constrained scenarios. Our code is released in https://github.com/FofGofx/WorldCache.
WorldCache is a caching framework for diffusion-based world models that accelerates inference through curvature-guided prediction and chaotic-prioritized skipping while maintaining high rollout quality.
diffusion-based world models iterative denoising feature caching token heterogeneity non-uniform temporal dynamics curvature-guided heterogeneous token prediction Hermite-guided damped predictor chaotic-prioritized adaptive skipping curvature-normalized drift signal