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The Differences Between Direct Alignment Algorithms are a Blur

Direct Alignment Algorithms (DAAs) offer a simpler way to language model alignment than traditional RLHF by directly optimizing policies. While DAAs differ in their use of SFT (one-stage vs. two-stage), the scalar scores within their objectives (likelihood vs. odds ratios), and ranking objectives (pairwise vs. pointwise), the critical factors for performance remain underexplored. We provide a systematic comparative analysis. We first show that one-stage methods (e.g. ORPO, ASFT) underperform compared to two-stage approaches. However, we demonstrate that adapting them to a two-stage setup with an explicit SFT phase can improve their performance. Further, introducing and tuning a unifying $\beta$ parameter within this two-stage framework boosts their performence (e.g., AlpacaEval 2: $+13.45$ ORPO, $+8.27$ ASFT), matching established methods like DPO and enabling fair comparisons. Our comprehensive analysis reveals that the choice between pairwise and pointwise objectives is the primary determinant of alignment success, rather than the specific scalar score (e.g., policy-reference ratio vs. odds ratio) employed. We provide empirical evidence suggesting this stems from how these objectives interact with prompt-specific biases. These findings underscore the need for nuanced evaluations in DAA research to avoid oversimplified claims of superiority.

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