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The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works

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arXiv:2605. 26246v1 Announce Type: new Abstract: Knowledge distillation (KD) transfers knowledge from a large teacher model to a smaller student.

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Authors: Guanghui Wang, Kaiwen Lv Kacuila, Zhiyong Yang, Zitai Wang, Jin-Wen Wu, Longtao Huang, Qianqian Xu, Qingming Huang
Released: 2026-05-27 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, soft, token, line, rate, full, trained, on-policy. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.26246

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