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English(EN) Cross-Epoch Adaptive Rollout Optimization for RL Post-Training

新的CERO方法通过自适应推广分配优化LLM强化学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为CERO的新方法，用于优化大型语言模型中的强化学习训练后阶段。与之前使用静态预算的方法不同，CERO将固定的推广预算自适应地分配给不同的提示。该方法利用提示成功概率的贝叶斯估计来确定额外推广的价值，从而提高了样本效率。实验表明，CERO在各种开源LLM的数学推理任务上优于现有方法。 AI

影响 提高了LLM训练的样本效率，可能导致更快地开发出更强大的模型。

排序理由 关于LLM训练后新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的CERO方法通过自适应推广分配优化LLM强化学习

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关于LLM训练后新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yiming Zong, Yige Wang, Jiashuo Jiang ·

    面向强化学习训练后跨时代自适应部署优化

    arXiv:2606.05606v1 Announce Type: new Abstract: LLM post-training often relies on reinforcement learning methods that sample multiple rollouts per prompt, yet most existing approaches use a fixed rollout budget for every prompt, despite large differences in the training signal di…

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