Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction
Using a statistical mechanics framework, we investigate the parameter space of transformer models trained on protein sequence data. We sample the loss landscape at varying temperatures using Langevin dynamics to characterize the low-loss manifold, and to understand the mechanisms underlying transformers' superior performance in protein structure prediction. We find that, at variance with networks not based on the attention mechanism, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties; this is true both for synthetic and natural protein sequences. We also show that the parameters of most lay...
| Authors | L. Ghiringhelli, A. Zambon, G. Tiana |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 31 March 2026 |
| Last updated at source | 22 September 2026 |
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