arXiv Machine Learning October 3, 2026 By Azal Ahmad Khan, Keshav Ramji, Tahira Naseem, Ali Anwar, Ram\'on Fernandez Astudillo

Learning to Predict Distributions over Weight Updates for Test-Time Adaptation

Read the original on arXiv Machine Learning →

The paper introduces query‑conditioned hypernetworks that predict distributions over LoRA weight updates for large language models. By learning a distribution rather than a single point estimate, the method allows sampling multiple adapted models for the same query, improving performance over deterministic hypernetworks and token‑sampling baselines. The study also shows that these learned updates can transfer across different queries, indicating reusable adaptation patterns.

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