@inproceedings{
malinovskii2024pvtuning,
title={{PV}-Tuning: Beyond Straight-Through Estimation for Extreme {LLM} Compression},
author={Vladimir Malinovskii and Denis Mazur and Ivan Ilin and Denis Kuznedelev and Konstantin Pavlovich Burlachenko and Kai Yi and Dan Alistarh and Peter Richt{\'a}rik},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=YvA8UF0I37}
}
There has been significant interest in "extreme" compression of large language models (LLMs), i.e. to 1-2 bits per parameter, which allows such models to be executed efficiently on resource-constrained devices.  
Existing work focused on improved one-shot quantization techniques and weight representations; yet, purely post-training  approaches are reaching diminishing returns in terms of the accuracy-vs-bit-width trade-off. State-of-the-art quantization methods such as QuIP# and AQLM include fine-tuning (part of) the compressed parameters over a limited amount of calibration data; however, such fine-tuning techniques over compressed weights often make exclusive use of straight-through estimators (STE), whose performance is not well-understood in this setting. 
In this work, we question the use of STE for extreme LLM compression, showing that it can be sub-optimal, and perform a systematic study of quantization-aware fine-tuning strategies for LLMs.
We propose PV-Tuning - a representation-agnostic framework that generalizes and improves upon existing fine-tuning strategies, and provides convergence guarantees in restricted cases.
On the practical side, when used for 1-2 bit vector quantization, PV-Tuning outperforms prior techniques for highly-performant models such as Llama and Mistral. 
Using PV-Tuning, we achieve the first Pareto-optimal quantization for Llama-2 family models at  2 bits per parameter.
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
Vladimir Malinovskii Denis Mazur Ivan Ilin Denis Kuznedelev Konstantin Pavlovich Burlachenko Kai Yi Dan Alistarh Peter Richtárik