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Sparse updates in on-policy distillation can match full performance with significantly reduced training overhead, challenging conventional wisdom about dense parameter updates.
LLMs still struggle to go beyond simple lookups when answering questions about tables, especially when prediction and reasoning about unobserved data is required.
