Ferdinand Kapl, Kaitlin Maile, Stefan Bauer, Johannes von Oswald · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31421 · open-access PDF
Looped and depth-grown language models reuse computations across depth and have attracted interest for their reasoning capabilities. Building on prior suggestive evidence of improved in-context-learning (ICL), we investigate how such reuse favors ICL, including how it shapes the acquisition, retention, and mechanisms of this ability. Our analysis spans pretrained language models up to 3B parameters and controlled tasks. The experiments provide evidence for a shared inductive bias toward learning from context: grown and looped models rely more on information in context and, compared with a full
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