From the source
From the source
Best practices for RL fine-tuning of Gemini models
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.
From the source
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.
cloud.google.com
