Scaling RNA Foundation Models through Iterative Refinement
Here we present Eco-RNA, an 80M-parameter RNA foundation model that makes inference depth adjustable through learned iterative refinement.
ProofPaper ↗
Key points
- Biological function in RNA is encoded across scales, from local sequence motifs to long-range structural dependencies.
- Conventional RNA foundation models couple parameter capacity and inference depth within a fixed architecture, prescribing a fixed computation graph for each inference.
- On the 70-assay RNAGym fitness benchmark, increasing refinement from one to four iterations raises aggregate absolute Spearman correlation from 0.242 to 0.286, with the gain retained through eight iterations.
- These results establish internal inference computation as an effective scaling dimension for RNA foundation models.
Sources (1)
- [1]Scaling RNA Foundation Models through Iterative RefinementarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 06:59 AM
Here we present Eco-RNA, an 80M-parameter RNA foundation model that makes inference depth adjustable through learned iterative refinement.
Biological function in RNA is encoded across scales, from local sequence motifs to long-range structural dependencies.
Extractive summary: sentences quoted from the sources.
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