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dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats

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arXiv:2606. 04115v1 Announce Type: new Abstract: Quantizing large language models (LLMs) to low-precision floating-point representations is central to efficient deployment, yet applying a single bit-width uniformly across all layers is sub-optimal in terms of both performance and accuracy.

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Authors: Giuseppe Franco, Ian Colbert, Pablo Monteagudo-Lago, Felix Marty, Nicholas Fraser
Released: 2026-06-05 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats 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: text, width, eval, project, without, language, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.04115

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