爱可可AI前沿快报 Vol.101

介绍了五项AI研究：1. 多元化奖励的CFG蒸馏，在不增加计算成本的前提下，提高AI生成内容的多样性和质量；2. 上下文强化学习，探索大型语言模型通过奖励信号自我优化学习新任务；3. 揭示了自动化评测大型语言模型的漏洞，即“空模型”也能获得高分；4.  发现重复训练少量样本可以提升模型在特定任务上的表现；5.  提出了一种新的采样算法——噪声校正朗格文算法，能够更高效地从无噪声分布中采样。完整推介：https://mp.weixin.qq.com/s/fzD99zz8BICTRrVPN9oMxg在小宇宙查看该单集文稿

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爱可可AI前沿快报 Vol.101

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