The Three Phases of Post-Training: How LLMs Learn to Provide Sensible Responses

Short summary

Modern LLMs use a three-stage post-training pipeline: Supervised Fine-Tuning teaches models to imitate good behavior, Reward Modeling trains an evaluator to recognize human preferences, and Reinforcement Learning optimizes outputs against this reward signal. Recent work shows that post-training data quality often matters more than model size, enabling smaller models to outperform larger ones through techniques like DPO and synthetic data generation.

• •Post-training has three phases: SFT (imitation teaching), RM (preference evaluation), RL (reward optimization)
• •Quality of feedback data often matters more than model parameters or size
• •Recent approaches like DPO and human-in-the-loop synthetic data improve training scalability

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