The 2026 default posttraining recipe is SFT → DPO → GRPO.

Turing Post's survey lays out the consolidated three-stage pipeline for tool-using agents: supervised fine-tuning for format and cold start, preference optimization (DPO/SimPO) for alignment, then RL with verifiable rewards (GRPO/DAPO) for reasoning and generalization. GRPO's practical draw is that it drops the value critic entirely and estimates advantages by group-wise comparison, so only the ordering of completions matters. Scores of 0.3/0.5/0.7 and 30/50/70 train identically. That makes hand-written reward functions dramatically more forgiving of bad scaling, which is where most homegrown reward code goes wrong.