Elastic transformers can run at different compute levels without retraining
A single model can adjust compute without retraining; deep pruning has limits and task variation
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Walter Simoncini, Dheeraj Varghese, Gertjan J. Burghouts, Cees G. M. Snoek
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Problem: models should run at different compute levels without retraining. Approach: researchers made a transformer that scales its internal paths as budgets change, keeping degradation gradual. Finding: some backbones withstand high sparsity; speed gains come with modest accuracy drops.
Why it matters
For business and government systems, elastic models could cut hardware costs and simplify deployment, but gains will vary by task and workload.
They built one model that can run with more or less compute by adjusting internal paths. The model degrades slowly, not suddenly, when compute is reduced.
In practice, you could switch budgets on the fly during busy hours without re-training, though very deep pruning may hurt some tasks like retrieval.
What remains uncertain
Deep pruning still harms some decoder-based text encoders; results vary by backbone and task, not a universal guarantee.
Original sources · 1
- Your Transformer Is Secretly Elastic ↗arXiv · 2026-10-14
Check the original paper for its authors, methods, version and access terms.
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