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arXiv Machine Learning By Nirmit Desai, Eric Song, Mayank Sengupta, Tejal Bedmutha, Siri Reddy, Sahiti Dharmavaram, Kunal Sawarkar

Kuration SDK: Addressing the Virtual2Real Gap via Data Curation

Read the original on arXiv Machine Learning →

The paper introduces Kuration SDK, a toolkit for curating raw gameplay data to address the Virtual2Real gap in action‑conditioned world models. It demonstrates that conventional metrics like FVD, LPIPS, and JEDi do not reliably predict playability, using CounterStrike data to show divergent model performance despite similar scores. By providing curation strategies and diagnostic tools, the SDK helps uncover root causes of the gap and accelerate the creation of sample‑efficient training datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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