The authors propose a differentiable Monte Carlo approximation to train a hypernetwork that outputs a distribution over LoRA updates for a query.
Single queries can predict useful weight update distributions for LLM adaptation
Distributional hypernetworks generate LoRA adapters from input queries alone, outperforming deterministic methods and enabling test-time scaling through weight sampling.
Big Tech
Azal Ahmad Khan · Keshav Ramji · Tahira Naseem · Ali Anwar · Ramón Fernandez Astudillo
University of Minnesota · IBM Research AI
Research Digest··2 min read
The authors introduce distributional hypernetworks, which predict a distribution over LoRA weight updates conditioned on a single input query.
Why this paper
From IBM Research AI and University of Minnesota
In one line
A single query provides enough signal to predict a distribution over LoRA updates, enabling test-time scaling via weight sampling.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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