From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning
Researchers propose PARPO, a reinforcement learning framework that decouples generic task rewards from user-specific preferences, enabling AI agents to adapt behavior across heterogeneous user needs. The work addresses a critical gap in agentic systems: current RL approaches optimize for universal correctness, but real-world deployments require personalized planning and tool-use strategies. By embedding personalization into training-time optimization rather than post-hoc adaptation, this framework tackles entanglement between task quality and conformity effects, opening pathways for agents that scale across diverse user populations without retraining. This matters for production agentic systems where one-size-fits-all policies fail.62
