A guide to LLM post-training

Large language model post-training adapts broadly pretrained base models to specific tasks through supervised fine-tuning, preference alignment, reinforcement learning, and distillation. Supervised fine-tuning teaches desired formats, instructions, tone, and task behavior from expert prompt-answer examples, while parameter-efficient approaches such as LoRA reduce cost by training small adapter matrices instead of all model weights. RLHF uses human-ranked outputs to train a reward model and then optimize the LLM, commonly with PPO, for subjective qualities such as helpfulness and safety, whereas DPO achieves preference alignment more simply by directly learning from chosen and rejected response pairs without a reward model or online reinforcement learning. RLVR uses automatically verifiable outcomes, such as correct answers or passing tests, and GRPO compares multiple generated responses without requiring a separate critic, making it well suited to coding, mathematics, tool use, and multi-step agents. Distillation trains a smaller student model to imitate outputs from a stronger teacher, with on-policy variants using privileged information or evaluation-selected self-generated answers when no superior teacher is available. In practice, these methods can form a pipeline, often beginning with SFT, followed by preference optimization and verifiable-reward reinforcement learning, while prompt engineering may be sufficient for simpler, narrowly defined failures.

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