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RL amplifies existing preferences in LLMs while also revealing previously hidden correct moves, reshaping our understanding of model training dynamics.
RL fine-tuning might be less about teaching LLMs new tricks and more about activating pre-existing "good vs. bad" representations lurking within them.
