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New paper examines sparse LLM parameter updates during post-training
The authors say they use Fisher information to connect theory with empirical findings across RL, SFT and on-policy distillation.
TLDR
The authors share a new paper examining why large language model parameter updates are sparse during post-training. They say it studies the pattern through Fisher information, connecting theory and empirical findings across reinforcement learning, supervised fine-tuning and on-policy distillation.
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