Kuration SDK：通过数据策管弥合 Virtual2Real 差距

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Abstract:Benchmarks for measuring the quality of action-conditioned world models are still evolving and shifting away from visual similarity-based metrics to action-semantic and physically-grounded metrics. However, for domain and task-agnostic action-conditioned world model training, existing benchmarks provide a limited signal. By training and evaluating diffusion world models on CounterStrike gameplay data, we confirm that qualitative playability does not correspond with metrics such as FVD, LPIPS, and JEDi. We term this the Virtual2Real gap. We posit that, in lieu of reliable benchmarks, curating raw gameplay data and measuring a variety of diagnostic properties provides a more robust signal to bridge the gap, before the training even begins. We present several curation strategies and a general-purpose kit for physical AI data curation called Kuration SDK, which is being open-sourced with this paper. The SDK was instrumental in uncovering the root cause of the virtual2real gap in a specific case: why two world models trained on identical gameplay map, action and state distribution, behaved very differently when played in spite of having very similar LPIPS and FVD scores. Thus, Kuration SDK has the potential to uncover the root causes of Virtual2Real gap in specific datasets and accelerate development of sample-efficient training datasets.

Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
MSC classes:	68T07, 68T40, 68T45
ACM classes:	I.2.10; H.3.3; I.4.8
Cite as:	arXiv:2610.09305 [cs.LG]
	(or arXiv:2610.09305v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.09305 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Nirmit Desai [view email]
[v1] Wed, 7 Oct 2026 02:03:08 UTC (267 KB)