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Computer Science > Machine Learning

arXiv:2502.01237 (cs)
[Submitted on 3 Feb 2025]

Title:The Differences Between Direct Alignment Algorithms are a Blur

Authors:Alexey Gorbatovski, Boris Shaposhnikov, Viacheslav Sinii,
Alexey Malakhov, Daniil Gavrilov
View a PDF of the paper titled The Differences Between Direct
Alignment Algorithms are a Blur, by Alexey Gorbatovski and 4 other
authors
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    Abstract:Direct Alignment Algorithms (DAAs) simplify language
    model alignment by replacing reinforcement learning (RL) and
    reward modeling (RM) in Reinforcement Learning from Human
    Feedback (RLHF) with direct policy optimization. DAAs can be
    classified by their ranking losses (pairwise vs. pointwise), by
    the rewards used in those losses (e.g., likelihood ratios of
    policy and reference policy, or odds ratios), or by whether a
    Supervised Fine-Tuning (SFT) phase is required (two-stage vs.
    one-stage). We first show that one-stage methods underperform
    two-stage methods. To address this, we incorporate an explicit
    SFT phase and introduce the $\beta$ parameter, controlling the
    strength of preference optimization, into single-stage ORPO and
    ASFT. These modifications improve their performance in Alpaca
    Eval 2 by +$3.46$ (ORPO) and +$8.27$ (ASFT), matching two-stage
    methods like DPO. Further analysis reveals that the key factor is
    whether the approach uses pairwise or pointwise objectives,
    rather than the specific implicit reward or loss function. These
    results highlight the importance of careful evaluation to avoid
    premature claims of performance gains or overall superiority in
    alignment algorithms.

Subjects: Machine Learning (cs.LG)
Cite as:  arXiv:2502.01237 [cs.LG]
          (or arXiv:2502.01237v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2502.01237
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Boris Shaposhnikov [view email]
[v1] Mon, 3 Feb 2025 10:54:14 UTC (118 KB)
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