| title | RAR: Rate-Adaptive Rollout for Time-Consistent Interactive World Models |
|---|---|
| authors | Yaoyang Liu, Dongyu Yan, Yihua Du, Shengju Qian, Zeke Xie, Xin Wang, Ying-Cong Chen, Long Chen |
| date | 2026-10-12 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.16302 |
| upvotes | 14 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | World models |
| abstract | Video-based interactive world models aim to enable real-time, user-controllable environments. Yet most existing systems are trained and rolled out at a fixed temporal rate, limiting both runtime-rate adaptation and physical-horizon coverage -- a limitation we term temporal-rate coupling. Specifically, when runtime generation throughput differs from the learned temporal rate, the generated world evolves unintentionally more slowly or quickly. Moreover, using a single temporal rate imposes an inherent trade-off between temporal granularity and physical horizon: higher rates capture finer motion but cover a shorter duration under a fixed context window. To address both limitations, we introduce Rate-Adaptive Rollout (RAR), a framework that enables interactive world models to roll out at controllable temporal rates. At its core, Stride-RoPE aligns latent frames sampled at different rates on a shared physical-time coordinate, enabling consistent multi-rate rollout, unseen-rate generalization, and online rate switching without discarding history. Meanwhile, RAR uses multi-stride video sampling to enable joint learning of fine-grained dynamics and long-horizon evolution. Across action- and camera-controlled environments, RAR achieves temporally consistent multi-rate rollout and online rate switching while preserving high-rate generation quality and extending physical horizons under matched training budgets. RAR also transfers to pretrained world models while preserving perceptual quality. |
cite
@misc{liu2026rar,
title={RAR: Rate-Adaptive Rollout for Time-Consistent Interactive World Models},
author={Yaoyang Liu and Dongyu Yan and Yihua Du and Shengju Qian and Zeke Xie and Xin Wang and Ying-Cong Chen and Long Chen},
year={2026},
eprint={2610.16302},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.16302}
}