FastForward Pruning: Efficient LLM Pruning via Single-Step Reinforcement Learning
This addresses the computational inefficiency of pruning LLMs for researchers and practitioners, though it is incremental as it builds on existing RL-based pruning approaches.
The paper tackled the problem of efficiently finding optimal non-uniform layer-wise sparsity allocations for pruning Large Language Models, proposing FastForward Pruning, which uses a decoupled single-step reinforcement learning framework to achieve competitive or superior performance at a fraction of the computational cost compared to other search-based methods.
Pruning is an effective method for compressing Large Language Models, but finding an optimal, non-uniform layer-wise sparsity allocation remains a key challenge. While heuristic methods are fast but yield suboptimal performance, more powerful search-based approaches like Reinforcement Learning are often hindered by prohibitive computational costs on large-scale models. To overcome this efficiency barrier, we propose FastForward Pruning. Its core is a decoupled, single-step RL framework that separates policy optimization from the complex budget satisfaction problem. Such a decoupling is crucial for efficiently searching the vast policy space of LLMs. This curriculum-based strategy begins with low-cost, simple tasks and gradually increases in complexity, significantly reducing the search's computational overhead. Evaluated on the LLaMA, Mistral, and OPT model families, our framework discovers pruning policies that achieve superior performance over strong heuristic baselines. Crucially, when compared to other search-based algorithms, our method achieves competitive or superior results at a fraction of the computational cost, demonstrating a clear advantage in search efficiency.