Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement
  Learning
Authors: name: Xin Qiu, name: Yulu Gan, name: Conor F. Hayes, name: Qiyao Liang, name: Elliot Meyerson, name: Babak Hodjat, name: Risto Miikkulainen

Fine-tuning pre-trained large language models (LLMs) for down-stream tasks is
a critical step in the AI deployment pipeline. Reinforcement learning (RL) is
arguably the most prominent fine-tuning method, contributing to the birth of
many state-of-the-art LLMs. In contrast, evolution strategies (ES), which once
showed comparable performance to RL on models with a few million parameters,
was neglected due to the pessimistic perception of its scalability to larger
models. In this work, we report the first successful attempt to scale up ES for
fine-tuning the full parameters of LLMs, showing the surprising fact that ES
can search efficiently over billions of parameters and outperform existing RL
fine-tuning methods in multiple respects, including sample efficiency,
tolerance to long-horizon rewards, robustness to different base LLMs, less
tendency to reward hacking, and more stable performance across runs. It
therefore serves as a basis to unlock a new direction in LLM fine-tuning beyond
what current RL techniques provide. The source codes are provided at:
https://github.com/VsonicV/es-fine-tuning-paper.