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cs.CL2026
How Depth Growth and Looping Shape In-Context Learning in Language Models
Ferdinand Kapl, Kaitlin Maile, Stefan Bauer +1
Looped and depth-grown language models reuse computations across depth and have attracted interest for their reasoning capabilities. Building on prior suggestive evidence of improv…
cs.CL2026
From Growing to Looping: A Unified View of Iterative Computation in LLMs
Ferdinand Kapl, Emmanouil Angelis, Kaitlin Maile +2
Looping, reusing a block of layers across depth, and depth growing, training shallow-to-deep models by duplicating middle layers, have both been linked to stronger reasoning, but t…
cs.CL2025
Do Depth-Grown Models Overcome the Curse of Depth? An In-Depth Analysis
Ferdinand Kapl, Emmanouil Angelis, Tobias Höppe +4
Gradually growing the depth of Transformers during training can not only reduce training cost but also lead to improved reasoning performance, as shown by MIDAS (Saunshi et al., 20…