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 set, PreDE calibrates two thresholds and accepts, rejects, or defers new configurations using a fix...
| Authors | Jiuyi Xu, Jinjia Guo, Meida Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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