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---[CL] Potemkin Understanding in Large Language Models[MIT & University of Chicago & Harvard University]https://arxiv.org/abs/2506.21521---[LG] The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas[Stanford University]https://arxiv.org/abs/2506.20803---[CL] Bridging Offline and Online Reinforcement Learning for LLMs[FAIR at Meta]https://arxiv.org/abs/2506.21495---[CL] Data Efficacy for Language Model Training[Microsoft Research]https://arxiv.org/abs/2506.21545在小宇宙查看该单集文稿