PUZZLE: Efficiently Aligning Large Language Models through Light-Weight Context Switching

Explore an innovative approach to efficiently aligning Large Language Models (LLMs) in this 15-minute conference talk from USENIX ATC '24. Delve into PUZZLE, a system designed to address the challenges of frequent context switching in LLM alignment. Learn how researchers from Tsinghua University tackle the overhead issues associated with parameter updates and data transfer during model and workload switching. Discover the two-dimensional approach employed by PUZZLE, focusing on intra- and inter-stage switching optimization. Understand how model affinities and time-sharing techniques are utilized to minimize switching costs within stages, and how a similarity-oriented strategy optimizes inter-stage switching. Gain insights into the performance improvements achieved by PUZZLE, demonstrating up to 2.12× speedup compared to the state-of-the-art RLHF training system DeepSpeed-Chat. This talk offers valuable knowledge for researchers and practitioners working on LLM alignment and efficient AI system development.

USENIX ATC '24 - PUZZLE: Efficiently Aligning Large Language Models through Light-Weight Context...