Olmo 3 – AI2’s latest open-source large language model series

What is Olmo 3?

Olmo 3 is a series of open-source large language models released by the Allen Institute for Artificial Intelligence (AI2). The lineup includes multiple variants: Olmo 3-Base (foundation models with 7B and 32B parameters) delivers strong performance in programming, reading comprehension, and mathematical problem-solving; Olmo 3-Think (reasoning model) focuses on complex reasoning and reinforcement learning; Olmo 3-Instruct (instruction-tuned model) excels at multi-turn dialogue and instruction following; and Olmo 3-RL Zero provides a reinforcement learning pathway.
Olmo 3 features powerful performance, efficient training, and a high degree of customizability, supporting a wide range of tasks—from coding to reasoning—and aims to advance explainability, collaborative innovation, and responsible AI development.

Key Capabilities

Robust Language Understanding and Generation:
The Olmo 3-Base model performs strongly across natural language processing tasks, including reading comprehension, math problem solving, and programming assistance.

Complex Reasoning and Logical Processing:
Olmo 3-Think is designed for multi-step reasoning tasks, handling complex math problems, code understanding, and logical inference, while also supporting long-context understanding.

Efficient Dialogue and Instruction Following:
Olmo 3-Instruct is optimized for conversations and instruction execution, enabling multi-turn dialogue, tool use (e.g., function calling), and task following—ideal for chatbots and intelligent assistants.

Reinforcement Learning Support:
Olmo 3-RL Zero provides a reinforcement learning framework that guides and optimizes complex behaviors starting from the base model, suitable for tasks requiring dynamic decision-making.

High Customizability:
Olmo 3 opens the entire model-development pipeline, supporting customization during pre-training, mid-training, and post-training, as well as integration of domain-specific knowledge.

Technical Foundations of Olmo 3

Multi-Stage Training Pipeline

• Pre-training:
  Trained on large-scale datasets (e.g., Dolma 3) to build broad language capabilities.

• Mid-training:
  Targets specific skill improvements such as mathematics, programming, and reading comprehension.

• Long-context training:
  Expands the model’s ability to understand long documents.

• Post-training:
  Further refined using supervised fine-tuning (SFT), preference optimization (DPO), and reinforcement learning (RL).

Decoder-Only Architecture

Olmo 3 uses a unidirectional decoder-only Transformer architecture, optimized for language generation and reasoning tasks.

Datasets and Tools

• Dolma 3:
  A massive 9.3-trillion-token corpus covering web pages, scientific papers, code, mathematical problems, and more.

• Dolci:
  A post-training dataset focused on reasoning, tool use, and instruction following.

• Data processing tools:
  Tools like datamap-rs and duplodocus are used for data cleaning, deduplication, and quality control.

Transparency and Traceability

Through the OlmoTrace tool, users can trace relationships between model outputs and training data, helping them understand how and why the model generates specific responses.

Efficient Training

Optimized training code and hardware utilization (e.g., H100 GPU clusters) significantly improve efficiency and reduce costs.

Olmo 3 Project Resources

Project Website: https://allenai.org/blog/olmo3
HuggingFace Models: https://huggingface.co/collections/allenai/olmo-3
Technical Report: https://www.datocms-assets.com/64837/1763662397-1763646865-olmo_3_technical_report-1.pdf

Applications of Olmo 3

Natural Language Understanding and Generation:
Useful for building intelligent writing assistants and content-generation tools that help produce high-quality text.

Complex Reasoning and Problem Solving:
Olmo 3-Think is well-suited for solving complex math tasks, difficult programming problems, and logical reasoning challenges—supporting research and education.

Dialogue Systems and Chatbots:
Olmo 3-Instruct handles multi-turn dialogue and instruction execution, making it ideal for intelligent customer service, virtual assistants, and interactive applications.

Reinforcement Learning and Dynamic Decision-Making:
Olmo 3-RL Zero provides reinforcement learning pathways for training agents that perform dynamic decision-making, such as robotics control or game AI.

Long-Text Processing and Information Retrieval:
Olmo 3 excels at understanding long documents, making it useful for processing reports, logs, and other extended texts.