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Olmo Improvement Benchmark

allenai.org · 3,951 words · saved by 1 readers

Language models are often treated as snapshots—brief captures of a long and carefully curated development process. But sharing only the end result obscures the rich context needed to modify, adapt, and extend a model's capabilities. Many meaningful adjustments require integrating domain-specific knowledge deep within the development pipeline, not merely at the final stage. To truly advance open AI development and research, the entire model flow – not just its endpoint – should be accessible and customizable. The model flow is the full lifecycle of an LM: every stage, checkpoint, dataset, and dependency required to create and modify it. By exposing this complete process, the goal is to engender greater trust and enable more effective adaptation, collaboration, and innovation. With today's release of Olmo 3, we're empowering the open source community with not only state-of-the-art open models, but the entire model flow and full traceability back to training data. At its center is Olmo 3-

Olmo 3: Charting a path through the model flow to lead open-source AI | Ai2 Olmo 3: Charting a path through the model flow to lead open-source AI November 20, 2025 Ai2 Share Models Tech Report Code API Demo Update 12/12: Announcing Olmo 3.1 Since the initial release of the Olmo 3 model flow, the team has been busy improving the reasoning and instruction-following capabilities of our models. The result is two new 32B checkpoints, our most performant to date: Olmo 3.1 Think 32B , the result of extending our best reinforcement learning (RL) run with a much longer training schedule. Olmo 3.1 Instr

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