openai · OpenAI Platform Docs

Reinforcement fine-tuning use cases

Explains how to apply reinforcement fine-tuning (RFT) to tasks that require verifiable outcomes, such as generating testable code, extracting structured facts from unstructured text, and applying complex rule-based po...

Import to Prompt Buddy

Derived skill

Files assembled from official documentation

Viewing SKILL.md

Reinforcement fine-tuning use cases

When To Use

Use when you need to improve model performance on tasks with deterministic or verifiable success criteria like code compilation, schema adherence, or factual extraction accuracy.

Reference Files

File	Contains	Use For
`SKILL.md`	Entry point: scope, routing table, and workflow.	Start here.
`docs/reinforcement-fine-tuning-use-cases-workflow-guide.md`	A guide outlining specific scenarios and practical applications for applying reinforcement fine-tuning to improve model performance on verifiable tasks.	Questions about a guide outlining specific scenarios and practical applications for applying reinforcement fine-tuning to improve mod...

What This Skill Covers

Reinforcement fine-tuning (RFT) provides a way to improve your model's performance at specific tasks. The task must be clear and have verifiable answers.
Main sections: When to use reinforcement fine-tuning, 1. Turn instructions into working code, Wiring verification IPs for semiconductor design, Production-ready API snippets that compile and pass AST checks, Correct handling of conflicts and dupes in a schedule manager.

Workflow

Open the most relevant file under docs/ for the exact documented workflow and wording.
Open schemas/ files for exact structured contracts.
Open examples/ files for concrete requests, commands, snippets, and manifests.
Do not add behavior or configuration that is not present in the attached source files.

Canonical source: https://developers.openai.com/api/docs/guides/rft-use-cases.md