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Post-norm normalization significantly enhances performance in LLMs when depth is introduced through a curriculum, outperforming pre-norm by an order of magnitude.
LLM reasoning can be compressed without sacrificing accuracy by selectively encouraging exploration only on difficult questions, preventing premature entropy collapse.
