The Differences Between Direct Alignment Algorithms are a Blur
Authors: name: Alexey Gorbatovski, name: Boris Shaposhnikov, name: Viacheslav Sinii, name: Alexey Malakhov, name: Daniil Gavrilov
Organization: T-Tech

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.