English(EN) PAC: Progress-Augmented Advantage Curriculum for Multi-Task Reinforcement Learning of LLMs

新的PAC课程增强了大型语言模型的强化学习能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种名为PAC（进度增强优势课程）的新方法，以改进大型语言模型（LLMs）的多任务强化学习。该方法结合了优势可学性信号和近期奖励增益，以动态分配不同任务的训练资源。通过跟踪策略更新潜力和实际性能改进，PAC旨在优化复杂推理场景中的样本效率和最终模型性能。 AI

影响这种新的课程方法可能导致更有效和更高效地训练大型语言模型以完成复杂的推理任务。

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arXiv cs.LG TIER_1 English(EN) · Yuanqiang Yu, Yanzhao Zheng, Zhentao Zhang, Tianze Xu, Chao Ma, Jihuai Zhu, Jiashun Liu, Xinle Deng, Baohua Dong, Hangcheng Zhu, Ruohui Huang · 2026-09-01 04:00

PAC：用于大型语言模型多任务强化学习的进度增强优势课程

arXiv:2608.30528v1 Announce Type: new Abstract: Reinforcement learning (RL) is used to improve the reasoning abilities of LLMs, while training data span heterogeneous tasks. However, most RL post-training pipelines rely on fixed or manually designed task mixtures, even though tas…