UIUC paper explains sparse LLM post-training updates through the lens of Fisher information
tokenbender · x · 2026-10-15
A new paper by Yufan Zhang, Sagnik Mukherjee, and Hao Peng (UIUC) studies why LLM parameter updates are so sparse during post-training, using Fisher information as the analytical lens. It connects theory with empirical findings across RL, SFT, and on-policy distillation, explaining which parameters actually change during fine-tuning and why — relevant to post-training dynamics and efficient fine-tuning.
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