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介绍了五项 AI 研究：1) KV-Compress 通过灵活压缩大语言模型的键值缓存，提升效率；2) 对比抽象学习让强化学习代理无需奖励即可学习抽象表示，提升学习效率；3) 研究发现大语言模型翻译时的冗余行为源于多种因素，现有评估方法对其不公；4) 模块化 GEM (mGEM) 通过精细控制模型参数更新，解决灾难性遗忘问题并提升泛化能力；5) 通过 SynTheory 数据集探究音乐生成模型对音乐理论的理解，发现模型大小并非决定性因素。完整推介：https://mp.weixin.qq.com/s/V_2-yst3r8YIRdvEsG1Vkw
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