User Preference as Reward Model
Do you want the carrot, or the stick?
Last updated
Do you want the carrot, or the stick?
Last updated
The system achieves scalable agent alignment by decoupling preference learning from environment dynamics, creating a self-reinforcing cycle of content creation and user feedback:
Observation. Goal Buddies maintain continuous awareness of their MetaSpace's subspaces, monitoring shifts in knowledge, trends, and user interests. This real-time observation enables them to identify opportunities for valuable content creation and respond to emerging patterns in their domains.
Policy. Goal Buddies refine their content generation strategies through iterative improvement. By observing which outputs gain traction with User Buddies and generate positive feedback from users, Goal Buddies continuously adapt their policies to produce more engaging and valuable content. On the other hand, User Buddies observe whether their chosen content is relevant to the user via direct and passive feedback from users, and adapt themselves via PBT to better align with their associated user.
Reward Model from User Feedback. User interactions generate a rich stream of feedback signals through natural engagement. Each like, share, or response contributes to building personalized preference models that capture individual user values and interests with increasing precision. The feedback will be provided to both Goal Buddies and User Buddies.
Reward Assignment. AiPP transforms these preference models into actionable reward signals, distributing them throughout the agent network. This creates a feedback loop where successful content generation strategies are reinforced, while less effective approaches are naturally filtered out.
This architecture enables scalable alignment by making preference learning an emergent property of the system. Rather than requiring centralized oversight, alignment emerges from the natural interaction between content creation, user engagement, and distributed feedback collection.
