Best practices guide for customizing Gemini models via Reinforcement Learning (RL)

Google Cloud has launched a managed reinforcement learning fine-tuning service (RLFT) that allows customers to adapt proprietary models like Gemini without needing direct access to model internals or large training infrastructure. The service enables users to define a reward function and prompts, letting the model learn from outcomes rather than fixed labeled answers. This approach addresses tasks that are difficult to demonstrate through examples but can be easily scored, such as generating valid SQL queries for varied schemas. The RLFT service automates the reinforcement learning process, generating multiple candidate responses, scoring them against the user-defined reward, and refining the model to favor higher-scoring outputs while preserving original capabilities.

The guide outlines best practices for using RLFT, including when to employ reinforcement learning over supervised fine-tuning (SFT). RLFT is particularly effective when grading responses is feasible but authoring them is not, or when tasks have multiple valid solutions that a single reference target would penalize. The service is designed to refine existing model competencies rather than teach entirely new skills, making it suitable for tasks where the base model already demonstrates partial success. Google recommends exhausting prompting and SFT approaches first, reserving RLFT for scenarios where SFT has plateaued or where outcome-based rewards are more practical than example-based training.

Google provides two workflows for RLFT: direct RLFT for tasks where the base model already succeeds part of the time, and a two-stage SFT → RLFT approach for cases with low initial success rates or existing SFT data. The two-stage method uses SFT as a lightweight warm start to prepare the model before transitioning to RLFT via Continuous Tuning, which initializes reinforcement learning from the SFT checkpoint. This hybrid approach helps avoid overfitting to demonstrations and maximizes the potential for RLFT to improve performance on the defined reward metric.

Early adopters of RLFT have reported measurable improvements in tasks such as maintaining in-character dialogue in multilingual, multi-turn conversations, extracting structured records from unstructured documents, and enforcing complex policy adherence at scale. These use cases demonstrate where RLFT delivers the most value by scoring outcomes that align with business objectives, such as reducing manual review steps, minimizing hallucinations, and preventing reward hacking in automated decision-making systems.