# Google Cloud — Best practices guide for customizing Gemini models via Reinforcement Learning (RL)

- Company: Google Cloud (cloud.google.com)
- Announced: 2026-09-25T16:00:00+00:00
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://cloud.google.com/blog/topics/developers-practitioners/best-practices-guide-for-customizing-gemini-models/
- Record: https://forck.live/items/17328-best-practices-guide-for-customizing-gemini-models-via-reinforcement-learning-rl
- Subject: Gemini Enterprise
- Models affected: Gemini

Google Cloud published a guide on best practices for using its managed RL fine-tuning (RLFT) service to customize Gemini models. The service allows users to adapt Gemini by defining a reward signal instead of providing labeled answers, and the guide covers when to use RLFT, how to design rewards, and example use cases such as NPC dialogue, entity extraction, content moderation, code execution, and slide generation.

## Evidence

Verbatim from https://cloud.google.com/blog/topics/developers-practitioners/best-practices-guide-for-customizing-gemini-models/:

> RLFT adapts Gemini from a reward signal you define rather than labeled answers. Instead of authoring a large set of gold examples, you write one program that scores a response and the service improves the model against it — unlocking tasks that are hard to demonstrate but easy to verify: you can't hand-write the ideal SQL for every schema, but you can run the query and check the result.

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