Default new alignment runs to DPO/ORPO/KTO, and use Reinforcement Fine-Tuning for verifiable tasks

hjLabs·medium signal

Production teams in 2026 have largely replaced PPO-RLHF with DPO and its descendants ORPO and KTO, which train directly on preference pairs via a closed-form contrastive loss — no reward model, no PPO instability, no rollout loop — at comparable quality for most tasks. For tasks with checkable answers (math, code, structured extraction), Reinforcement Fine-Tuning (RFT) rewards the model for getting verifiable outcomes right rather than imitating a reference answer. Start from a LoRA/QLoRA run on an open-weight model, which now costs a few hundred dollars on a single high-memory GPU versus the $50K+ clusters of 2022.