95
0[CL] Can Gradient Descent Simulate Prompting?
[MIT CSAIL]
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[CL] Potemkin Understanding in Large Language Models
[MIT & University of Chicago & Harvard University]
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[LG] The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas
[Stanford University]
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[CL] Bridging Offline and Online Reinforcement Learning for LLMs
[FAIR at Meta]
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[CL] Data Efficacy for Language Model Training
[Microsoft Research]
