model merging — 2 Technical Articles

Machine Learning Algorithms & Natural Language Processing

Apr 18, 2026 · Artificial Intelligence

Model Ability Gets Squeezed Out in Multi‑Task Learning—How ESM Preserves It (CVPR 2026)

The paper reveals that multi‑task models suffer performance drops because tasks compete for the same internal subspace, and introduces Essential Subspace Merging (ESM) which separates critical directions and uses Polarized Scaling to keep multiple abilities stable, achieving significantly lower degradation than traditional baselines.

ESDESMMulti-Task Learning

0 likes · 16 min read

Model Ability Gets Squeezed Out in Multi‑Task Learning—How ESM Preserves It (CVPR 2026)

Baobao Algorithm Notes

Apr 16, 2024 · Artificial Intelligence

Merging Large Language Models Without GPUs: Task Vector, SLERP, TIES & DARE Explained

This article introduces four advanced model‑merging algorithms—Task Vector, SLERP, TIES, and DARE—explains their underlying principles, compares their strengths, and demonstrates a practical merge of Mistral‑7B, WizardMath‑7B and CodeLlama‑7B using the open‑source MergeKit toolkit.

AIDAREMergeKit

0 likes · 10 min read

Merging Large Language Models Without GPUs: Task Vector, SLERP, TIES & DARE Explained

Model Ability Gets Squeezed Out in Multi‑Task Learning—How ESM Preserves It (CVPR 2026)

Merging Large Language Models Without GPUs: Task Vector, SLERP, TIES & DARE Explained

Model Ability Gets Squeezed Out in Multi‑Task Learning—How ESM Preserves It (CVPR 2026)