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Gemini RL fine-tuning trains against reward functions instead of labeled answers
Google’s managed RL fine-tuning service adapts Gemini from a user-defined reward signal, targeting tasks that are difficult to demonstrate but easy to score. The guide covers when to use RL and how to get value from this post-training approach.
- What
- Google Cloud's managed RL fine-tuning service adapts Gemini using prompts and a customer-defined reward function instead of fixed labeled answers.
- How
- At each step, the service generates candidate responses, scores them with the reward, and improves the model to favor higher-scoring outputs while staying close to Gemini.
- Why it matters
- Reward-based tuning suits tasks that are hard to demonstrate but easy to score, such as checking whether generated SQL queries return the right result.
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