Fine-Tuning Language Models with Just Forward Passes
Authors: name: Sadhika Malladi, name: Tianyu Gao, name: Eshaan Nichani, name: Alex Damian, name: Jason D. Lee, name: Danqi Chen, name: Sanjeev Arora

Fine-tuning language models (LMs) has yielded success on diverse downstream
tasks, but as LMs grow in size, backpropagation requires a prohibitively large
amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients
using only two forward passes but are theorized to be catastrophically slow for
optimizing large models. In this work, we propose a memory-efficient
zerothorder optimizer (MeZO), adapting the classical ZO-SGD method to operate
in-place, thereby fine-tuning LMs with the same memory footprint as inference.
For example, with a single A100 80GB GPU, MeZO can train a 30-billion parameter
model, whereas fine-tuning with backpropagation can train only a 2.7B LM with
the same budget. We conduct comprehensive experiments across model types
(masked and autoregressive LMs), model scales (up to 66B), and downstream tasks
(classification, multiple-choice, and generation). Our results demonstrate that
(1) MeZO significantly outperforms in-context learning and linear probing; (2)
MeZO achieves comparable performance to fine-tuning with backpropagation across
multiple tasks, with up to 12x memory reduction; (3) MeZO is compatible with
both full-parameter and parameter-efficient tuning techniques such as LoRA and
prefix tuning; (4) MeZO can effectively optimize non-differentiable objectives
(e.g., maximizing accuracy or F1). We support our empirical findings with
theoretical insights, highlighting how adequate pre-training and task prompts
enable MeZO to fine-tune huge models, despite classical ZO analyses suggesting
otherwise.