Reward Model.

A reward model is a neural network trained to predict human preferences by scoring AI-generated outputs based on human feedback data. These models serve as automated judges that can evaluate the quality, helpfulness, harmlessness, and alignment of AI responses at scale. Reward models are essential components in modern AI training pipelines, particularly for creating AI assistants that behave in ways humans find useful and appropriate.

The reward model training process involves collecting human preference data where evaluators compare pairs of AI outputs and indicate which response is better. This preference data trains a model to predict a scalar reward score for any given output, essentially learning to approximate human judgment. During reinforcement learning, the AI policy is then optimized to maximize these predicted reward scores, allowing the system to generate responses that align with human preferences without requiring direct human feedback for every output.

Reward models enable scalable alignment but introduce important limitations and considerations. The quality of the reward model directly impacts the final AI system's behavior, and poorly trained reward models can lead to reward hacking where the AI exploits weaknesses in the scoring system rather than genuinely improving. Additionally, reward models may not capture the full complexity of human values and can perpetuate biases present in the training data, making careful design and evaluation critical for building trustworthy AI systems.