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
The proliferation of Large Language Models (LLMs) necessitates efficient distillation techniques, and research is actively addressing the trade-offs between compression and model capabilities.
This research highlights a critical vulnerability in current LLM distillation methods, indicating that highly efficient, smaller models may lose complex reasoning abilities, which is crucial for advanced AI applications.
The understanding of LLM distillation is refined, revealing an inherent challenge in preserving reasoning during compression, which will guide future research and development towards more robust efficient models.
- · AI researchers
- · Developers of specialized LLMs
- · Cloud computing providers
- · Companies relying on overly aggressive LLM distillation
- · General-purpose smaller LLMs
More sophisticated and nuanced distillation techniques will be developed to explicitly address reasoning preservation.
The development and deployment of smaller, more efficient LLMs for complex tasks may be temporarily slowed until reasoning collapse is mitigated.
This could lead to a bifurcation in LLM use, with larger models retained for critical reasoning tasks and distilled models for specific, less complex applications.
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Read at arXiv cs.LG