AI前沿：从梯度下降模拟提示到数据效能的革命

[CL] Can Gradient Descent Simulate Prompting?

[MIT CSAIL]

https://arxiv.org/abs/2506.20989

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[CL] Potemkin Understanding in Large Language Models

[MIT & University of Chicago & Harvard University]

https://arxiv.org/abs/2506.21521

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[LG] The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas

[Stanford University]

https://arxiv.org/abs/2506.20803

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[CL] Bridging Offline and Online Reinforcement Learning for LLMs

[FAIR at Meta]

https://arxiv.org/abs/2506.21495

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[CL] Data Efficacy for Language Model Training

[Microsoft Research]

https://arxiv.org/abs/2506.21545