AI systems that learn from user feedback often face a gradual decline in feedback quality and quantity over time. This phenomenon, known as feedback loop decay, happens when users grow tired of providing input, when the same users repeatedly contribute similar feedback, or when the system stops incorporating new input effectively. Early warning signs include:

• Diminishing response rates to feedback requests
• Inconsistent quality of submitted feedback
• Stagnating system performance metrics despite continued user engagement

Models trained with reinforcement learning from human feedback (RLHF) are particularly dependent on feedback quality. They can only improve to the extent that feedback accurately guides them toward better outputs.

Teams need to establish baseline metrics for healthy feedback loops and regularly monitor key indicators such as feedback diversity, user participation rates, and the impact of feedback on model performance.

Without this vigilance, AI systems risk becoming stagnant, repeating the same patterns and mistakes. Addressing decay requires refreshing feedback collection methods, engaging new user segments, changing the presentation of feedback requests, and sometimes temporarily increasing incentives.

Some platforms implement rotating feedback mechanisms, presenting different formats to prevent user fatigue. Others use adaptive scheduling that adjusts feedback frequency based on individual user behavior rather than bombarding everyone with the same requests.