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 revisit the general rea...
He-Nan Sun, Ze-Hua Li, Hai-Tao Hu et al.
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