The Two Pillars of Post-training: Reinforcement Learning and Supervised Fine-Tuning

# The Two Pillars of Post-training: Reinforcement Learning and Supervised Fine-Tuning

Post-training has become the critical phase that transforms raw language models into usable assistants. Sharon Zhou's series examines the mechanics behind this transformation, focusing on two fundamental techniques that shape model behavior after initial training completes.

Supervised fine-tuning represents the first pillar. This approach uses curated datasets of high-quality examples to teach models specific behaviors and response patterns. Rather than leaving a model to generate text based purely on statistical patterns learned during pretraining, supervised fine-tuning steers outputs toward desired formats, tones, and domains. Organizations apply this technique to make models follow instructions more reliably, maintain consistency in their voice, and handle specialized tasks like coding or legal analysis.

Reinforcement learning from human feedback (RLHF) forms the second pillar and adds a feedback mechanism beyond static training data. This technique trains reward models using human preferences, then uses those models to guide the language model toward outputs humans actually prefer. Unlike supervised fine-tuning's fixed examples, RLHF creates an ongoing optimization loop that adapts as human evaluators rate different outputs.

The distinction matters practically. Supervised fine-tuning scales easily but relies on the quality and breadth of training examples. Teams must anticipate use cases and create representative data. RLHF captures human judgment directly but requires building robust reward models and managing the complexity of training multiple systems in tandem.

Modern LLMs typically employ both techniques in sequence or combination. Supervised fine-tuning establishes baseline behavior and handles clear-cut patterns. RLHF then refines outputs to align with subtler human preferences around helpfulness, harmlessness, and honesty. Recent models from Anthropic, OpenAI, and others blend these approaches, sometimes adding additional layers like constitutional