"Understanding LLM Parameter Update Sparsity through the Lens of Fisher" LLM post-training methods like RL and on-policy distillation often produce highly sparse parameter updates, but why? This paper uses the diagonal Fisher information to explain this: parameters that barely affect the model's output probabilities tend to receive smaller gradients. It also shows that Fisher can identify these parameters before training begins. On Qwen3-1.7B, updating just 0.1% of parameters selected by…