From Growing to Looping: A Unified View of Iterative Computation in LLMs
#LLM architecture #looping #depth growing #iterative computation #reasoning #depth‑wise signatures #layer reuse #mechanistic unification #arXiv preprint #February 2026
📌 Key Takeaways
- Looping reuses a block of layers across the network depth.
- Depth‑growing trains shallow‑to‑deep models by duplicating middle layers.
- Both strategies have been linked to improved reasoning capabilities.
- The paper provides a mechanistic unification of the two approaches.
- Looped and depth‑grown models share depth‑wise signatures: more reliance on late layers and recurring patterns tied to the reused or duplicated block.
- Shared signatures suggest a common underlying mechanism for iterative computation in LLMs.
📖 Full Retelling
The arXiv preprint titled "From Growing to Looping: A Unified View of Iterative Computation in LLMs" (arXiv:2602.16490v1, submitted February 2026) investigates how two architectural strategies—looping a block of layers across depth and depth‑growing by duplicating middle layers—both lead to stronger reasoning in large language models. The study presents a mechanistic unification, showing that looped and depth‑grown models exhibit convergent depth‑wise signatures, such as increased reliance on late layers and recurring patterns aligned with the reused or duplicated block. The authors therefore aim to clarify the relationship between these two approaches and explain why they jointly enhance model reasoning.
🏷️ Themes
Large Language Models (LLMs), Model architecture, Iterative computation, Layer reuse, Depth‑growing techniques, Reasoning in neural networks, Mechanistic explanation of model behavior, Neural network depth analysis
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Original Source
arXiv:2602.16490v1 Announce Type: cross
Abstract: 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 their relationship remains unclear. We provide a mechanistic unification: looped and depth-grown models exhibit convergent depth-wise signatures, including increased reliance on late layers and recurring patterns aligned with the looped or grown block. These shared sign
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