文章解析大模型偏好对齐算法 DPO 与 GRPO 的演进

On 2026-11-13 at 08:39, Juejin published an article titled "A Simplified and Evolutionary Analysis of DPO and GRPO, the Preference Alignment Algorithms for Large Models", which sets out to trace how preference alignment methods for large models have evolved. The piece frames the trajectory as a move from RLHF toward DPO and GRPO. Within the earlier RLHF pipeline, the article explains, a Reward Model is trained first to imitate human preference scoring, and the PPO (Proximal Policy Optimization) algorithm is then applied to advance training. Building on that framing, the article explains how DPO simplifies the conventional reward model training procedure and sets out the optimization approach GRPO takes during the reinforcement learning stage. The author presents the write-up as suited to developers who want to grasp the core mechanics of alignment algorithms. At this point, this topic is covered by this single report, and no further developments appear in the available material, so the record stands at the moment of this article's publication.