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Computer Science > Machine Learning

arXiv:2401.01335 (cs)
[Submitted on 2 Jan 2024]

Title:Self-Play Fine-Tuning Converts Weak Language Models to Strong
Language Models

Authors:Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan
Gu
Download a PDF of the paper titled Self-Play Fine-Tuning Converts
Weak Language Models to Strong Language Models, by Zixiang Chen and
Yihe Deng and Huizhuo Yuan and Kaixuan Ji and Quanquan Gu
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    Abstract: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.

Comments: 28 pages, 6 figures, 6 tables
          Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
Subjects: Computation and Language (cs.CL); Machine Learning
          (stat.ML)
Cite as:  arXiv:2401.01335 [cs.LG]
          (or arXiv:2401.01335v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2401.01335
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Quanquan Gu [view email]
[v1] Tue, 2 Jan 2024 18:53:13 UTC (833 KB)
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