PAWS: New Method Improves Preference-Based Reinforcement Learning Through Segment-Level Advantage Functions
Researchers have developed PAWS, a preference-based reinforcement learning method that trains policies using segment-level advantage functions rather than per-step utility estimates. The approach addresses a fundamental mismatch between how existing methods train utility functions and how they apply them during policy optimization, which causes distribution shift and degrades learning. The work demonstrates improved performance on robotic manipulation and locomotion tasks, suggesting the method could advance how AI systems learn from human preferences without explicit reward design.
PAWS (Preference Learning with Advantage-Weighted Segments) is a new approach to preference-based reinforcement learning that learns policies from human comparisons of trajectories rather than requiring explicit reward functions or expert demonstrations. The key innovation addresses a technical problem in existing methods: they train utility functions at the trajectory or segment level but apply per-step utility estimates during policy optimization, creating a distribution shift that impairs credit assignment and policy learning. PAWS resolves this by aligning training and inference—performing policy updates directly with segment-level advantage functions that preserve trajectory-level preference information. Experiments on simulated robotic tasks show PAWS consistently outperforms existing preference-based approaches, underscoring the importance of maintaining consistency between how a system learns preferences and how it uses them to optimize behavior.
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- arXiv cs.LGCenter
PAWS: Preference Learning with Advantage-Weighted Segments
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