Building a closed loop of human feedback reinforcement learning
Aivilization has designed a three-tiered data collection system:
- active intervention level: the user directly modifies the intelligences' decisions (e.g., resetting the task priority) through the console, and the system records the difference in status before and after the modification as a comparison sample
- Behavioral evaluation layer: triggers a 5-level scoring interface (from "completely wrong" to "ideal solution") after an intelligent body completes a complex task, asking the user to mark specific points for improvement.
- social consensus level: When multiple users make similar corrections to the same type of behavior, the system automatically increases the weight of that feedback, forming a group intelligence distillation
Best practices: 1) Use the "annotation function" to justify changes at the time of intervention 2) Prioritize participation in the platform's annotationsHigh-value mission scenarios(Tasks showing data collection flags) 3) Regularly check the Contribution Board to see how the feedback you provide is being applied to model updates.
This answer comes from the articleAivilization: a social simulation sandbox exploring human-AI coexistenceThe
May not be reproduced without permission:AI productivity tools " How to optimize the quality of training data for AI models through Aivilization?
