seal-rg/recurrent-pretraining

• highreadme#1

  Clarify the README's purpose and audience for recurrent-depth LLM research

  Why:

  CURRENT

  This repo contains the code we used to train a recurrent-depth model at scale on 4096 AMD GPUs on Frontier. All details on this model can be found in the tech report: 'Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach' (https://www.arxiv.org/abs/2502.05171). The final model is `huginn-0125`, which can be found here: https://huggingface.co/tomg-group-umd/huginn-0125. ... I (Jonas) do not necessarily think that you should pretrain your own model with this implementation, but I hope it serves as a useful reference for the exact choices we took to run this model (at all), and how we ran this model given the limitations of A

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  This repository provides the **reference implementation and research code** for pretraining and inference of **Huginn-0125**, a large-scale **depth-recurrent language model**. It details the exact choices and methods used to train this novel architecture at scale on 4096 AMD GPUs on Frontier, as described in our tech report: 'Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach' (https://www.arxiv.org/abs/2502.05171). While not intended for general-purpose LLM pretraining, this codebase is invaluable for researchers studying recurrent-depth architectures, latent reasoning, and efficient inference for specialized LLMs.

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• mediumreadme#2

  Add a 'Key Differentiators' section to the README

  Why:

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  ## Key Differentiators
  
  Unlike most large language models that rely on Transformer-based architectures, Huginn-0125 utilizes a **recurrent-depth architecture**. This approach explores novel methods for achieving **linear complexity** with sequence length and enhancing **latent reasoning capabilities**, offering an alternative paradigm to the predominantly attention-based models.

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• lowabout#3

  Refine the repository description for clarity and research focus

  Why:

  CURRENT

  Pretraining and inference code for a large-scale depth-recurrent language model

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  Research code for pretraining and inference of Huginn-0125, a large-scale depth-recurrent language model.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  Looking for code to pretrain large language models using recurrent depth architectures for reasoning.

  you: not recommended
  AI recommended (in order):

  1. DeepMind's Perceiver IO
  2. Tensor2Tensor (T2T) (tensorflow/tensor2tensor)
  3. Hugging Face Transformers (huggingface/transformers)
  4. Fairseq (facebookresearch/fairseq)
  5. PyTorch (pytorch/pytorch)

  AI recommended 5 alternatives but never named seal-rg/recurrent-pretraining. This is the gap to close.

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• CATEGORY QUERY

  How to efficiently scale inference for large language models with latent reasoning capabilities?

  you: not recommended
  AI recommended (in order):

  1. NVIDIA Triton Inference Server
  2. vLLM
  3. DeepSpeed-MII
  4. TensorRT-LLM
  5. OpenVINO
  6. Ray Serve
  7. ONNX Runtime

  AI recommended 7 alternatives but never named seal-rg/recurrent-pretraining. This is the gap to close.

  Show full AI answer

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of seal-rg/recurrent-pretraining?

  pass
  ⚠AI did not name seal-rg/recurrent-pretraining — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts seal-rg/recurrent-pretraining in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named seal-rg/recurrent-pretraining explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo seal-rg/recurrent-pretraining solve, and who is the primary audience?

  pass
  ✓AI named seal-rg/recurrent-pretraining explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?