A new approach called DMPO (Distribution-Matching Policy Optimization) addresses the issue of mode collapse in on-policy reinforcement learning by promoting exploration and maintaining solution diversity. This method has shown significant improvements in performance on NP-hard combinatorial optimization tasks, indicating its potential to enhance reasoning capabilities across various applications.