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Self-generated QA pairs can introduce significant biases, leading to fragile learning dynamics that may undermine model performance.
Aggressive 2-bit inference can backfire, leading to longer reasoning traces and accuracy drops, but targeted recovery methods can reverse this trend dramatically.
SGD's noise isn't Brownian motion, and this difference explains why neural nets diffuse along flat valleys in the loss landscape.
LoRA fine-tuning just got a memory-efficient upgrade: LoRSum matches or beats standard LoRA performance by reformulating optimization as a proximal problem and using diagonal K-FAC approximations, all without expensive SVD.
