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Computer Science > Computation and Language

arXiv:2412.15287 (cs)
[Submitted on 18 Dec 2024]

Title:Inference-Aware Fine-Tuning for Best-of-N Sampling in Large
Language Models

Authors:Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang,
Bo Dai, Sridhar Thiagarajan, Craig Boutilier, Rishabh Agarwal, Aviral
Kumar, Aleksandra Faust
View a PDF of the paper titled Inference-Aware Fine-Tuning for
Best-of-N Sampling in Large Language Models, by Yinlam Chow and 9
other authors
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    Abstract:Recent studies have indicated that effectively utilizing
    inference-time compute is crucial for attaining better
    performance from large language models (LLMs). In this work, we
    propose a novel inference-aware fine-tuning paradigm, in which
    the model is fine-tuned in a manner that directly optimizes the
    performance of the inference-time strategy. We study this
    paradigm using the simple yet effective Best-of-N (BoN) inference
    strategy, in which a verifier selects the best out of a set of
    LLM-generated responses. We devise the first imitation learning
    and reinforcement learning~(RL) methods for BoN-aware
    fine-tuning, overcoming the challenging, non-differentiable
    argmax operator within BoN. We empirically demonstrate that our
    BoN-aware models implicitly learn a meta-strategy that
    interleaves best responses with more diverse responses that might
    be better suited to a test-time input -- a process reminiscent of
    the exploration-exploitation trade-off in RL. Our experiments
    demonstrate the effectiveness of BoN-aware fine-tuning in terms
    of improved performance and inference-time compute. In
    particular, we show that our methods improve the Bo32 performance
    of Gemma 2B on Hendrycks MATH from 26.8% to 30.8%, and pass@32
    from 60.0% to 67.0%, as well as the pass@16 on HumanEval from
    61.6% to 67.1%.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Machine Learning (cs.LG)
Cite as:  arXiv:2412.15287 [cs.CL]
          (or arXiv:2412.15287v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2412.15287
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Yinlam Chow [view email]
[v1] Wed, 18 Dec 2024 20:43:47 UTC (1,342 KB)
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