| title | QQWorld: Quantile-Quantile Matching for World Model Regularization |
|---|---|
| authors | Zhoushun Yu, Xiaoyu Hu, Xiangyu Xu |
| date | 2026-07-30 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.28415 |
| upvotes | 20 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| primer | World models |
| abstract | Latent world models enable efficient planning by predicting future states in a compact representation space, but their performance depends critically on the quality of the learned latent distribution. LeWorldModel (LeWM) regularizes its latents toward an isotropic Gaussian using the Epps-Pulley (EP) objective. We show that the corrective gradients of EP rapidly vanish for isolated tail samples, leaving heavy-tailed deviations insufficiently controlled. To address this limitation, we propose QQWorld, which replaces EP with a quantile-quantile matching objective that directly aligns projected latent samples with rank-matched Gaussian quantiles, thereby maintaining effective corrective gradients in the tails. We further develop cross-batch QQ, which enlarges the effective ranking pool using detached samples from previous batches, and characterize its bias-variance trade-off. Across four control environments, QQWorld effectively improves the average planning success rate of LeWM, while consistently yielding better Gaussian alignment and thinner latent tails. |
cite
@misc{yu2026qqworld,
title={QQWorld: Quantile-Quantile Matching for World Model Regularization},
author={Zhoushun Yu and Xiaoyu Hu and Xiangyu Xu},
year={2026},
eprint={2607.28415},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.28415}
}