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New York University
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Emergent capabilities in transformer models arise stochastically, with larger models gaining critical skills earlier due to their ability to learn sparse attention patterns more effectively.
LCLMs redefine the efficiency of long-context inference, achieving superior compression without sacrificing model quality.
RL fine-tuning might be less about teaching LLMs new tricks and more about activating pre-existing "good vs. bad" representations lurking within them.
