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Fine-tuning low-bit models can be both efficient and deployment-faithful, thanks to a new optimization technique that leverages code surrogate gradients.
LLM knowledge editing often fails because the "fix" only works when you ask about it in the same way it was trained, revealing a surprising brittleness in how models incorporate new information.
