新的SCARL方法将语言模型置信度与奖励对齐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-31 04:00

研究人员推出了一种新颖的强化学习技术SCARL，旨在改进语言模型的推理能力。SCARL通过将策略对响应的置信度与其实际奖励对齐，解决了可验证奖励（RLVR）强化学习中的置信度不对齐问题。该方法旨在确保模型高度自信的响应也获得更高的奖励，反之亦然。实验表明，SCARL在Pass@32等基准测试中提高了性能，并在各种估计器和模型骨干上增强了覆盖率和每抽样准确率，同时还保持了策略熵。 AI

影响该方法可以提高语言模型在复杂推理任务中的可靠性和准确性。

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arXiv cs.AI TIER_1 English(EN) · Zhenghong Ke, Jingbo Zhou, Hua Wu · 2026-10-31 04:00

SCARL：序列置信度对齐强化学习

arXiv:2610.40407v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning by scoring sampled responses with a rule-based verifier and reinforcing those that pass. Where a classifier's training couples predicted proba…

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SCARL：序列置信度对齐强化学习

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