Reward Model
A Reward Model is a neural network trained to score responses generated by an LLM based on human preferences (e.g., helpfulness, safety, format correctness). It is used as the scoring engine in reinforcement learning loops like RLHF.
Frequently Asked Questions
How is a reward model trained?▼
By feeding it a dataset of prompt-response pairs where human annotators have selected which response is preferred (chosen vs. rejected).
Why is a reward model separate from the main LLM?▼
To provide an independent, scalar score that the main LLM can use to update its weights during reinforcement learning.
Reward Model Media Coverage & Intelligence
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