Gemini, do you have RLHF, or learning with human feedback?

 Yes, I (Gemini) am trained using Reinforcement Learning from Human Feedback (RLHF).

It is a core part of how Google aligns its models to be more helpful, accurate, and safe. RLHF is the "fine-tuning" phase that happens after my initial training on vast amounts of data.

Here is a breakdown of how it works and why it matters:

1. How RLHF works for Gemini

The process generally follows these steps:

• Supervised Fine-Tuning (SFT): Human experts write out high-quality examples of how a model should respond to specific prompts. This gives the model a baseline for "good" behavior.

• Ranking Responses: Humans are shown multiple different responses to the same prompt and asked to rank them from best to worst based on criteria like helpfulness, honesty, and safety.

• Reward Model: Those rankings are used to train a separate "reward model." This model learns to predict what a human would find valuable.

• Reinforcement Learning: Finally, the main model (Gemini) "plays" against the reward model. It generates millions of responses, and the reward model gives it points (rewards) for good ones. This trains the model to consistently choose the path that humans prefer.

2. Why it is necessary

Without RLHF, a Large Language Model (LLM) is essentially just a very powerful "next-word predictor." It might know a lot of facts, but it wouldn't necessarily know:

• How to be polite: It might repeat toxic language found on the internet.

• How to follow instructions: It might just continue your sentence instead of answering your question.

• How to be safe: It might provide dangerous information because that information exists somewhere in its training data.

3. Gemini-Specific Innovations

Google also uses a variation called RLAIF (Reinforcement Learning from AI Feedback). In this version, a highly capable "teacher" AI helps evaluate the responses of the model being trained. This allows Google to scale the training process much faster than relying on human labelers alone.

For the Gemini 2.0 Flash Thinking models, RLHF is even used to fine-tune the "reasoning process" (the "thoughts" you see before the answer), ensuring that the logic I use is sound and follows human-like common sense.

Would you like to know more about the specific safety guidelines that humans use when they rate my responses?

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