Jacobian-guided Noise Injection for Quantization Robustness in Large Language Models
Quantization of Large Language Models (LLMs) is often hindered by the sensitivity of the self-attention mechanism to discretization errors. We identify the softmax operator as a bottleneck for quantization stability due to its sensitivity to outliers and state-dependent Jacobian. We theoretically establish that suppressing the norm of this Jacobian helps in bounding quantization-induced performance degradation. Based on this, we propose Jacobian-Guided Noise Injection, a training strategy that injects zero-mean Gaussian noise into pre-attention logits, with variance derived directly from the Jacobian Frobenius norm. Unlike prior approaches that rely on heuristic or penalise jacobian direc...
| Authors | Deepanshu Pandey, Arnav Chavan, Nahush Lele et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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