Reasoning-preserved Efficient Distillation of Large Language Models via Activation-aware Initialization

arXiv:2605.29327v1 Announce Type: cross Abstract: Efficient Distillation (EDistill) compresses large language models (LLMs) by structured pruning parameters and tuning lightweight modules with high training efficiency. Although these EDistilled LLMs achieve state-of-the-art (SOTA) performance on general ability benchmarks relative to similarly sized LLMs, we identify a severe degradation in their multi-step reasoning ability, which we term reasoning collapse. We systematically analyze the geometric origins of reasoning collapse and show that the SOTA EDistill method based on width-reducing pro

Source: arXiv cs.LG — read the full report at the original publisher.

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