arXiv:2510.18784v3 Announce Type: replace Abstract: Despite significant work on low-bit quantization-aware training (QAT), there is still an accuracy gap between such techniques and native training. To address this, we introduce CAGE (Curvature-Aware Gradient Estimation), a new QAT method that augments the straight-through estimator (STE) gradient with a curvature-aware correction designed to counteract the loss increase induced by quantization. CAGE is derived from a multi-objective view of QAT that balances loss minimization with the quantization constraints, yielding a principled correction
The continuous push for more efficient AI models, especially at the edge, drives innovation in quantization techniques to bridge the accuracy gap with native training.
Improved quantization-aware training methods like CAGE increase the practical deployability of AI models on resource-constrained hardware, accelerating AI accessibility and application.
The ability to run high-performing AI models with significantly reduced computational cost and memory footprint on everyday devices is enhanced.
- · Edge AI hardware manufacturers
- · AI software developers
- · Deep learning practitioners
- · Mobile computing
- · Companies reliant solely on high-compute cloud AI
Reduced energy consumption and cost for deploying AI models at scale.
Faster AI inference in real-time applications leading to new product capabilities and user experiences.
Democratization of advanced AI capabilities beyond large data centers towards pervasive, integrated intelligence.
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Read at arXiv cs.LG