Harnessing the power of human-annotated data through Supervised Fine-Tuning
(SFT) is pivotal for advancing Large Language Models (LLMs). In this paper, we
delve into the prospect of growing a strong LLM out of a weak one without the
need for acquiring additional human-annotated data. We propose a new
fine-tuning method called Self-Play fIne-tuNing (SPIN), which starts from a
supervised fine-tuned model. At the heart of SPIN lies a self-play mechanism,
where the LLM refines its capability by playing against instances of itself.
More specifically, the LLM generates its own training data from its previous
iterations, refining its policy by discerning these self-generated responses
from those obtained from human-annotated data. Our method progressively
elevates the LLM from a nascent model to a formidable one, unlocking the full
potential of human-annotated demonstration data for SFT. Theoretically, we
prove that the global optimum to the training objective function of our method
is achieved only when the LLM policy aligns with the target data distribution.
Empirically, we evaluate our method on several benchmark datasets including the
HuggingFace Open LLM Leaderboard, MT-Bench, and datasets from Big-Bench. Our
results show that SPIN can significantly improve the LLM's performance across a
variety of benchmarks and even outperform models trained through direct
preference optimization (DPO) supplemented with extra GPT-4 preference data.
This sheds light on the promise of self-play, enabling the achievement of
human-level performance in LLMs without the need for expert opponents.

通过自我对弈优化学习，无需专家对手，实现在大型语言模型中无需人工标注数据即可达到人类水平性能的研究