Post-training

As explained in Build a Reasoning Model (From Scratch)

Methods applied after initial pretraining to improve model behavior or capabilities.

Post-training refines a pretrained language model after next-token learning, improving instruction following, human-preference alignment, or reasoning behavior without rebuilding the model from raw text. section 1.2 section 1.5

A common workflow applies instruction fine-tuning, reasoning training, and preference tuning after pretraining, although stages may be reordered, interleaved, or selectively skipped. section 1.2 section 6.1

Post-training methods serve distinct engineering goals:

• Instruction fine-tuning makes responses follow user prompts more directly. section 1.2
• Preference tuning improves qualities such as tone, engagement, and alignment with human preferences. section 1.2
• Reinforcement learning strengthens reasoning-like behavior through action outcomes, with reasoning training and preference tuning as common stages. section 1.5 section 6.1

One prompt across training stages

For a sleep-and-health summary, a pretrained model gives a relevant but unfocused answer; instruction tuning produces a concise response, while preference tuning adds a friendly, engaging tone. section 1.2

Figure 1.3 Overview of a typical LLM training pipeline. The process begins with a model initialized with random weights, followed by pretraining on large-scale text data to learn language patterns by predicting the next token. Post-training then refines the model through instruction fine-tuning and preference fine-tuning, enabling the LLM to better follow human instructions and align with human preferences.

Use the pipeline diagram to trace how pretraining leads into instruction fine-tuning and preference fine-tuning. section 1.2

How Build a Reasoning Model (From Scratch) covers it

How deep it goes

Distinguishes major stages and their ordering but defers detailed reinforcement-learning implementation to later treatment.

Examples

Compares pretrained, instruction-tuned, and preference-tuned answers to a sleep-and-health summary prompt.

What it assumes

Assumes familiarity with pretrained language models while treating advanced mathematics as unnecessary for following the reasoning methods.

What you can do after

Evaluate how training stages change instruction following, response style, and reasoning-like behavior.

How it connects

Connects pretraining, instruction fine-tuning, preference tuning, reinforcement learning, reasoning training, and inference-time scaling.

liveBookmanning.com

Read Build a Reasoning Model (From Scratch) on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Reinforcement Learning from Human FeedbackPost-training is the set of optimization and adaptation procedures applied to a pretrained model to improve its usefulness for downstream tasks or alignment objectives.
• 

  liveBookmanning.com
  Introduction to Generative AI, Second EditionPost-training is the adaptation of a pretrained model using additional task-specific data and optimization rather than training the model from scratch.

Compare how 3 books explain post-training →

Related concepts

• fine-tuninghas part · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.
• foundational modeluses · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
• reinforcement learninghas part · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
• RLAIFincludes · Reinforcement learning from AI feedback is a training or alignment technique that uses AI-generated evaluations or preferences as feedback to optimize a model or agent.
• Direct Preference Optimizationincludes · Direct Preference Optimization is a technique for training a model directly from preference data, such as preferred and dispreferred outputs, without separately fitting a reward model.
• reinforcement learning with human feedbackhas part · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.

This explainer was generated by Manning's knowledge graph from the text of Build a Reasoning Model (From Scratch). The section links point to the parts of the book it draws on.