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Southeast University, Nanyang Technological University
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Leveraging hidden states from reward models can boost RLHF performance by over 6% on challenging benchmarks, transforming how we utilize reward signals.
RL can unlock better compositional generalization than supervised fine-tuning by directly optimizing for correct outcomes, especially on complex tasks where supervised models overfit.
