Which Decisions Low-Bit Quantization Breaks, and How to Predict Them
Quantization saves memory by storing model weights with fewer bits. It can also change model decisions, such as whether to call a tool or which option to choose from a finite set. We study these decision changes in 16 language models from 8 families at 4, 3 and 2 bits, across several post-training quantization settings. Our evaluation covers tool use, safety, general knowledge and social bias, using BFCL, XSTest, MMLU, BoolQ, BBQ and synthetic tasks. The decision margin is the score difference between two possible first tokens, measured before and after quantization. Writing the margin before quantization as $m$ and the margin after quantization as $m'$, we find an approximately linear re...
| Authors | Zekun Wu, Swati Dhiman, Adriano Koshiyama |
|---|---|
| Primary category | cs.LG |
| Published | 6 August 2026 |
| Last updated at source | 5 October 2026 |
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