We introduce Dory1.0-4B, a 4-billion-parameter looped (latent-recurrent) language model with elastic depth: a single checkpoint serves different inference compute budgets. Dory1.0-4B is built on a nor
Dory1.0-4B is a 4-billion-parameter recurrent language model with elastic depth that improves accuracy and token efficiency by looping a shared block, outperforming larger models on multiple benchmarks.
We introduce Dory1.0-4B, a 4-billion-parameter looped (latent-recurrent) language model with elastic depth: a single checkpoint serves different inference compute budgets. Dory1.0-4B is built on a normalized Transformer (nGPT) backbone with interleaved sliding-window and full-attention layers, organized into an input block, a shared recurrent block, and an output block. At inference, the recurrent block can be applied a variable number of times. We pre-trained Dory1.0-4B on 6 trillion tokens. The first 4.5 trillion tokens were trained in NVFP4, and additional pre-training then extended the context length to 256K tokens. Next, we post-trained the model using Supervised Fine-Tuning (SFT) and…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Dory1.0-4B: A Looped Normalized-GPT was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 16 checks on 3 different days. Its best position was #19 of 30, reached on Nov 6, 01:28 UTC. It dropped off the list after Nov 6, 03:28 UTC.
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Not at the moment. It was last on the list on Nov 6, 03:28 UTC, after 3 days of trending.
#19 on Hugging Face · Daily papers, on Nov 6, 01:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 4, 2026 and has been on the list on 3 days (16 checks) since.