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GRRR: The Geometry of Reshaping, Rotation, and Routing in Decoder LLM post-training
We study how post-training changes the weights of Large Language Models (LLMs) relative to their pretrained weights. Across 12 post-training chains with supervised fine-tuning (SFT) and reinforcement learning (RL), we express each weight update in the pretrained matrix's singular value decomposition
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