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Cross-sample consistency regularization can eliminate feature fragmentation in Sparse Autoencoders, leading to more reliable and interpretable latent representations.
Aggressive off-policy RL for LLMs plateaus because excessive updates cause unnecessary drift, but distilling the resulting policy under trust-region constraints unlocks further gains.
