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Olmo 3

Summary

Most competitive language models publish only their final weights. This hides the data and intermediate steps that shaped them. Olmo 3 addresses this by releasing the complete "model flow" for 7B and 32B models: every training stage, checkpoint, dataset, code dependency and log. The base model is trained in three phases: broad pretraining, a midtraining stage aimed at reasoning, math and code, and a long-context extension built largely on science PDFs. From this base, several post-trained variants are derived:

  • Think, which writes step-by-step reasoning traces.
  • Instruct, which gives short, direct answers suited to chat and function calling.
  • RL-Zero, which applies reinforcement learning directly to the base model.

The team also built new data (Dolma 3, Dolci) and tooling (OlmoBaseEval, OlmoRL). Olmo 3.1 Think 32B is reported as the best fully-open thinking model so far. It approaches Qwen 3 32B while using roughly six times fewer training tokens. Because everything is open, model reasoning can be traced back to the training data, which makes the release a reproducible research testbed.

Key points

  • Base training budget: up to 5.9T pretraining tokens, 100B midtraining tokens, and 50B (7B) or 100B (32B) long-context tokens.
  • Released data:
    • Dolma 3 Mix for pretraining.
    • Dolma 3 Dolmino Mix for midtraining.
    • Dolma 3 Longmino Mix for long context.
    • The Dolci suite for post-training (SFT, DPO and RL sets for Think, Instruct and RL-Zero).
  • Base model results: Olmo 3 Base 32B outperforms the fully-open baselines Stanford Marin, Apertus and LLM360 K2-V2.
  • Instruct results: Olmo 3 Instruct beats Qwen 2.5, Gemma 3, IBM Granite 3.3 and Llama 3 at comparable sizes.
  • RL-Zero experiments:
    • Training with random rewards gave no benchmark gains, which supports successful decontamination.
    • Active sampling in OlmoRL kept batches full and reduced loss variance.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing · AI & machine learning

Cited by

104 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 99 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2512.13961 · arXiv 2512.13961

@misc{ettinger2025olmo,
  title = {{Olmo 3}},
  author = {Ettinger, Team Olmo: Allyson and Bertsch, Amanda and Kuehl, Bailey and Graham, David and Heineman, David and Groeneveld, Dirk and Brahman, Faeze and Timbers, Finbarr and Ivison, Hamish and Morrison, Jacob and Poznanski, Jake and Lo, Kyle and Soldaini, Luca and Jordan, Matt and Chen, Mayee and Noukhovitch, Michael and Lambert, Nathan and Walsh, Pete and Dasigi, Pradeep and Berry, Robert and Malik, Saumya and Shah, Saurabh and Geng, Scott and Arora, Shane and Gupta, Shashank and Anderson, Taira and Xiao, Teng and Murray, Tyler and Romero, Tyler and Graf, Victoria and Asai, Akari and Bhagia, Akshita and Wettig, Alexander and Liu, Alisa and Rangapur, Aman and Anastasiades, Chloe and Huang, Costa and Schwenk, Dustin and Trivedi, Harsh and Magnusson, Ian and Lochner, Jaron and Liu, Jiacheng and Miranda, Lester James V. and Sap, Maarten and Morgan, Malia and Schmitz, Michael and Guerquin, Michal and Wilson, Michael and Huff, Regan and Bras, Ronan Le and Xin, Rui and Shao, Rulin and Skjonsberg, Sam and Shen, Shannon Zejiang and Li, Shuyue Stella and Wilde, Tucker and Pyatkin, Valentina and Merrill, Will and Chang, Yapei and Gu, Yuling and Zeng, Zhiyuan and Sabharwal, Ashish and Zettlemoyer, Luke and Koh, Pang Wei and Farhadi, Ali and Smith, Noah A. and Hajishirzi, Hannaneh},
  year = {2025},
  doi = {10.48550/arxiv.2512.13961},
  eprint = {2512.13961},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2512.13961}
}

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