essay · 2026-10-10

Post-training

Recent research on large language model post-training reveals that many teams stop at Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), yet these methods often fail to handle complex, multi-stage tasks reliably. A key finding is that while RLHF provides a quality jump, it is frequently described as "expensive and finicky" due to the difficulty in training stable reward models for nuanced behaviors. In contrast, Group Relative Policy Optimization (GRPO) offers a direct preference optimization approach that eliminates the need for an intermediate value model, making it more efficient for tasks requiring strict adherence to rules or verifiable outcomes. For instance, one technical guide explicitly compares methods like LoRA, QLoRA, DPO, and GRPO as essential components of modern fine-tuning stacks in 2026. Another source notes that while SFT turns a base model into a chatbot, RLVR (Verifiable Rewards) is better suited for establishing correctness and structure before any preference tuning occurs. The core takeaway is that relying on a single stage like DPO often leads to error accumulation, whereas a pipeline incorporating verifiable rewards yields more robust per

Sources

• Video Generation Models: A Survey of Post-Training and Alignment — 2026-09-30T23:13:08Z
• RLVR: Verifiable Rewards for Reliable Enterprise LLMs | Appen
• Training a Large Language Model (LLM) (2026) · Linh Truong
• LLM Post-Training Guides: SFT, DPO, GRPO, RLHF & RLVR · LLMix
• An introduction to reinforcement learning from human feedback and...
• LLM Fine-Tuning 2026: LoRA, QLoRA, DPO, GRPO