# Looped Transformer (Looped depth)

> The same blocks are applied several times, reusing weights across depth.

Category: Residual stream

With L distinct blocks applied T times, the model gets L × T effective layers for the parameter cost of L. It is depth-wise weight tying, sometimes called recurrent depth.

Nanbeige 4.2 uses two fixed passes, Ouro uses four passes with a learned exit gate, and Dory1.0 loops a shared middle stack.

**Trade-off:** More compute per parameter; extra passes add latency and, depending on the design, KV cache.

## Models using Looped depth (3)

- [Dory1.0 (4B)](https://llm-atlas-7or.pages.dev/models/dory1-0-4b.md) — NVIDIA, 2026-10-16
- [Nanbeige 4.2 (3B)](https://llm-atlas-7or.pages.dev/models/nanbeige-4-2-3b.md) — BOSS Zhipin, 2026-07-21
- [Ouro-Thinking (2.6B)](https://llm-atlas-7or.pages.dev/models/ouro-thinking-2-6b.md) — ByteDance, 2025-10-29

Full explainer with figures by Sebastian Raschka: https://sebastianraschka.com/llm-architecture-gallery/looped-depth-sharing/
Web page: https://llm-atlas-7or.pages.dev/concepts/looped-depth-sharing/