SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs
This story is from 2026-08-27. It is preserved in the archive; the latest stories are on the live feed.
arXiv:2608.25068v1 Announce Type: new Abstract: Depth pruning removes entire Transformer blocks to reduce the inference cost of large language models, but disrupts the hidden-state distributions expected by downstream layers, leading to significant accuracy loss. We introduce SHIFT-LLM, a training-…
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- 2026-08-27 04:00 · arXiv cs.CV
SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs
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