How post-training turns a capable model into a useful assistant
A practical map of supervised demonstrations, preference data, reward modeling, reinforcement learning, and the evaluation loop around them.
Pretraining gives a language model broad capability. It does not, by itself, define how the model should respond to an instruction, resolve competing preferences, or behave when a request crosses a boundary. Post-training is where those expectations become data, objectives, and tests.
Sharon Zhou’s O’Reilly Radar article offers a useful walkthrough of this transition. We traced the core process back to the published InstructGPT work and translated it here into an operating model for teams building or adapting their own AI systems.
Process map
A simplified public post-training loop
The arrows are iterative in practice: evaluation findings feed new prompts, examples, comparisons, and specifications.
- 01Specify
Define useful behavior
Write the instructions, examples, and boundaries that make “good” behavior concrete enough for people to judge.
Output: Behavior specification - 02Demonstrate
Show strong responses
Human labelers produce high-quality answers to representative prompts, creating examples the model can imitate.
Output: Demonstration dataset - 03Supervise
Fine-tune the model
Supervised fine-tuning shifts the base model toward the demonstrated response style and task behavior.
Output: SFT policy - 04Compare
Rank candidate outputs
People compare several answers to the same prompt. Those rankings capture preference more efficiently than writing every answer.
Output: Comparison dataset - 05Model
Learn a preference signal
A reward model learns to predict which responses people are more likely to prefer, turning rankings into a scalable training signal.
Output: Reward model - 06Optimize
Improve, evaluate, repeat
Reinforcement learning improves the policy against that signal while evaluations check capability, safety, regressions, and drift.
Output: Evaluated assistant
Why the loop matters
The published InstructGPT recipe combines three distinct jobs. Supervised fine-tuning gives the model a strong behavioral starting point. Human comparisons capture preferences between plausible outputs. A reward model then makes that preference signal available at training scale.
The final reinforcement-learning stage optimizes the policy against that learned signal. In the paper, a penalty constrains the policy from drifting too far from its supervised starting point. That detail is important: optimization can exploit an imperfect score, so the objective needs both pressure and bounds.
The method improved human preference ratings in the reported evaluations, but it did not make the model fully aligned or eliminate mistakes. The durable lesson is the system around the optimizer: explicit behavior targets, carefully produced data, held-out judgment, and repeated evaluation.
Recohut perspective
Three lessons for production teams
Treat behavior as a specification
“Helpful” is not an executable requirement. Teams need examples, counterexamples, refusal boundaries, and evaluation criteria before choosing a training technique.
Treat preference data as a product
The quality of the comparison task, labeler guidance, disagreement handling, and coverage matters as much as the optimization algorithm that consumes the data.
Put evaluation around every stage
A better aggregate reward is not enough. Teams need held-out evaluations for task success, safety, calibration, regressions, and important user segments.
The work continues after training
A model can score well offline and still fail inside a real product. Production behavior also depends on prompts, tools, retrieval, permissions, latency, fallbacks, monitoring, and the decisions people make around the model. Those layers need their own evidence and controls.
That is why we treat post-training as one part of a wider delivery loop: define the behavior, measure it on representative work, deploy it with bounded authority, observe failures, and turn only reviewed evidence into the next iteration.
From idea to operation
Adapting or deploying a model?
Recohut helps teams benchmark the workload, design the evaluation path, and deploy open-weight models with the reliability and cost controls production requires.