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DCRL: Decoupling and Coupling Reinforcement Learning via Policy-Reward Manifold Alignment
Reinforcement learning (RL) has emerged as a key paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing reward systems, such as rule-based and reward-model-based, often exhibit issues such as unstable optimization and reward hacking. In this work, we rev
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