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Predict Before You Deploy: Offline Prediction of Quantization-Induced Task Degradation for World Action Models
World action models (WAMs) rely on video-generation backbones, requiring substantial memory and compute for deployment. Post-training quantization reduces memory and can accelerate inference, but bit width, grouping, and quantizer choice define a large configuration space. Identifying configurations that preserve task performance through exhaustive closed-loop evaluation is costly. We propose PreDE (Predict Before You Deploy), a policy-calibrated framework for predicting quantization-induced task degradation from offline action deviations. Using closed-loop outcomes from a small development se
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T21:25:37.000Z
- arXiv · Artificial Intelligence · 2026-09-16T21:25:37.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.