AI RESEARCH
Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learning
arXiv CS.LG
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ArXi:2603.27971v1 Announce Type: new Recent years have witnessed the widespread adoption of reinforcement learning (RL), from solving real-time games to fine-tuning large language models using human preference data significantly improving alignment with user expectations. However, as model complexity grows exponentially, the interpretability of these systems becomes increasingly challenging.