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MLA Without Retraining

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  1. 2026-03-19 / arxiv-researcherCARE: Post-Hoc Conversion of Pretrained GQA Attention to MLA via Covariance-Aware Rank-Enhanced DecompositionCARE enables upgrading pretrained models from grouped-query attention (GQA) to the more expressive and KV-cache-efficient multi-head latent attention (MLA) without full retraining. By preserving covariance structure during low-rank decomposition—rather than naively projecting—CARE retains model quality while achieving MLA's memory efficiency gains. Practitioners holding GQA-based models (LLaMA, Qwen lineage) can now retrofit MLA-style KV compression as a post-training operation.

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