本期《TAI快报》深入探讨了AI领域的五项前沿研究：1.《Small Models, Smarter Learning: The Power of Joint Task Training》揭示联合任务训练能让小型模型通过学习更“聪明”的算法显著提升效率；2.《Efficient Data Selection at Scale via Influence Distillation》提出“影响蒸馏”方法，以更低成本挑选高效训练数据；3.《Hybrid Latent Reasoning via Reinforcement Learning》通过强化学习让模型自主融合推理与生成能力；4.《Learning to Reason without External Rewards》展示AI如何仅靠自身“自信”信号提升推理与泛化能力；5.《The Limits of Preference Data for Post-Training》从理论上揭示偏好数据的固有局限，尤其在复杂推理任务中的不足。这些发现为AI的训练策略、数据效率及自主学习开辟了新思路。完整推介：https://mp.weixin.qq.com/s/kAlrckiyP55jDc-wRbbC0A

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