Post-Training, RLHF, and Reasoning Models

When you select a foundation model for an application, raw text completion is only the starting point. Out of the box, a base model knows how to predict words, but it does not know how to act like a specific product, follow interface contracts, or protect user safety.

Post-trainingThe critical phase in Large LLM development that happens after initial pre-training to shape behavior, alignment, and task performance. techniques and runtime reasoning controls bridge that gap, transforming general capabilities into a dependable service. However, sounding helpful is not the same as being accurate. A customer service bot might write an empathetic message approving an expired refund simply because it was trained to sound pleasant rather than verify company policy.

In this lesson, you will learn how Supervised Fine-TuningA machine learning process that adapts a pre-trained base language model by training it further on a curated, labeled dataset of instruction-and-response pairs. and Reinforcement Learning from Human Feedback (RLHF)A machine learning technique that aligns artificial intelligence models, especially LLMs, with human values, safety, and intentions by using human preferences as a guiding signal. shape model behavior, why alignment differs from factual truth, and how to manage the latency and cost of reasoning models. Instruction tuning is a primary form of SFT and serves as the foundational post-training phase in production deployments.

From base model to aligned assistant

Instruction tuning is the simplest way to make a pretrained model usable in a request pipeline. During training, the training pipeline feeds instruction–response pairs to the model, and the model updates its weights to produce that style of answer directly at ...

What the tuning data changes

A ...