Author

Shih-Cheng Huang

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#machine learning Preprint Oct 2026

A Broader Look at Model Merging: Rethinking Implicit Regularization Induced by Task Arithmetic

Model merging aims to build a multi-task model cheaply by combining the weights of individual task-specific models. To perform well across multiple tasks, most existing merging methods use an additional dataset to find the coefficients for the best linear combination of task-specific weight updates. However, we identif...

Sin-Han Yang, Shih-Cheng Huang, Chieh-Yen Lin et al. · 0 citations

#machine learning Preprint Oct 2026

$\alpha$Transfer: Coefficient Transfer for Efficient Model Merging

Model merging offers a promising solution for combining multiple fine-tuned checkpoints into a single model through parameter arithmetic. However, finding optimal merging coefficients requires an extensive search that becomes prohibitively expensive as models scale in both size and number, due to high memory requiremen...

Shih-Cheng Huang, Zhi Rui Tam, Chieh-Yen Lin et al. · 0 citations

#artificial intelligence Conference Apr 2025

Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors

Experiments show that the proposed Preference Vector framework improves helpfulness without excessive conservatism, allows smooth control over preference trade-offs, and supports scalable multi-preference alignment.

Ren-Wei Liang, Chin-Ting Hsu, Chan-Hung Yu et al. · Volume 1 · 7 citations