Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908924939075584 |
|---|---|
| author | Ghiringhelli, L. Zambon, A. Tiana, G. |
| author_facet | Ghiringhelli, L. Zambon, A. Tiana, G. |
| contents | We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize the low-loss manifold and understand the mechanisms underlying the superior performance of transformers in protein structure prediction. We find that, at variance with feedforward networks, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties. We show that the parameters of most layers are highly conserved at these temperatures if the dimension of the embedding is optimal, and we provide an operative way to find this dimension. Finally, we show that the attention matrix is more predictive of the contact maps of the protein at higher temperatures and for higher dimensions of the embedding than those optimal for learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29529 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction Ghiringhelli, L. Zambon, A. Tiana, G. Disordered Systems and Neural Networks Machine Learning Biomolecules We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamics to characterize the low-loss manifold and understand the mechanisms underlying the superior performance of transformers in protein structure prediction. We find that, at variance with feedforward networks, the lack of a first--order--like transition in the loss of the transformer produces a range of intermediate temperatures with good learning properties. We show that the parameters of most layers are highly conserved at these temperatures if the dimension of the embedding is optimal, and we provide an operative way to find this dimension. Finally, we show that the attention matrix is more predictive of the contact maps of the protein at higher temperatures and for higher dimensions of the embedding than those optimal for learning. |
| title | Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction |
| topic | Disordered Systems and Neural Networks Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2603.29529 |