Implicit user signals (eye gaze, mouse movement) can substantially improve LLM reward models and alignment, suggesting that behavioral data is a practical alternative to expensive explicit human feedback collection.
This paper shows that user behavior signals like mouse movements and eye gaze contain valuable information about LLM response quality.