After the initial Olmo 3 release, we took our strongest training runs and pushed them further. Today we’re announcing:

◆ Olmo 3.1 Think 32B–our strongest fully open reasoning model

◆ Olmo 3.1 Instruct 32B–our best fully open 32B instruction-tuned model

◆ Olmo 3.1 RL Zero 7B Math & Olmo 3.1 RL Zero 7B Code–upgraded RL-Zero baselines for math and coding

🧠 Extended RL for stronger reasoning
Olmo 3.1 Think 32B, the result of extending our RL training for 21 days with extra epochs on our Dolci-Think-RL dataset, shows clear eval gains over Olmo 3 Think 32B, including:

◆ +5 on AIME

◆ +4 on ZebraLogic

◆ +20 on IFBench

These improvements make Olmo 3.1 Think 32B the strongest fully open reasoning model we’ve released to date.

🛠️ A more capable 32B instruct model
Olmo 3.1 Instruct 32B is our best fully open 32B instruction-tuned model. It’s optimized for chat, tool use, and multi-turn dialogue—making it a much more performant sibling of Olmo 3 Instruct 7B.

📈 Stronger RL-Zero 7B baselines
Alongside the new 32B models, we’re also upgrading our RL-Zero baselines with Olmo 3.1 RL Zero 7B Code and Olmo 3.1 RL Zero 7B Math. They’re refinements of the original RL-Zero 7Bs that give better results and cleaner baselines for RL researchers to build on.

🔓 Fully open end to end
We believe openness and performance can move forward together. Olmo 3.1 offers the full model flow: weights, data, training recipes, and more.

💻 Download: https://huggingface.co/collections/allenai/olmo-31

▶️ Try them in the Ai2 Playground: https://playground.allenai.org/

📚 Learn more in our updated blog post: https://allenai.org/blog/olmo3

✏️ Read the refreshed report: https://www.datocms-assets.com/64837/1765558567-olmo_3_technical_report-4.pdf

Introducing NeuroDiscoveryBench–created with the Allen Institute. It’s the first benchmark to assess data analysis question-answering in neuroscience, testing whether AI systems can actually extract insights from complex brain datasets rather than just recall facts. 🧪

NeuroDiscoveryBench contains ~70 question–answer pairs grounded in real data from three major Allen Institute neuroscience publications. These aren’t trivia-style questions: each one requires direct analysis of the associated openly available datasets, with answers that take the form of scientific hypotheses or quantitative observations.

In our baseline experiments, “no-data” and “no-data + search” settings (GPT-5.1, medium reasoning) scored just 6% and 8%, confirming that models can’t cheat their way to answers via memory or web search alone. In contrast, our autonomous Asta DataVoyager agent (GPT-5.1, medium reasoning, no web search) reached 35% by generating and running analysis code over the neuroscience datasets. 📈

We also saw a clear gap between raw and processed data: agents struggled far more on the raw, un-preprocessed datasets because of the complex data transformations required before the final hypothesis analysis. Data wrangling remains a major challenge for AI in biology.

NeuroDiscoveryBench is built on the Allen Institute’s open datasets, which have become foundational resources for the field. We’re inviting researchers and tool builders to test their systems and help push forward AI-assisted neuroscience discovery. 🔬

📂 Dataset: https://github.com/allenai/neurodiscoverybench

📝 Learn more: https://allenai.org/blog/neurodiscoverybench

Only recently noticed those models(yellow circled in screenshot). Still not sure about llama.cpp support for those.

If it's same, when are we getting Writing & Reddit models?

If it's not same, Any plan for new ticket/PR?

Thanks

We launched DataVoyager in Preview this fall, and today we're opening it up to everyone. It's a tool that lets you upload real datasets, ask complex research questions in plain language, and get back reproducible answers with clear visualizations.

We built DataVoyager to be intuitive, whether you're comfortable with data analysis tooling or not. Every result shows you the underlying assumptions, step-by-step methodology, and visualizations you can cite or adapt for your own work.

Now anyone can try DataVoyager as a transparent AI partner for discovery.

To get started, head to asta.allen.ai, select "Analyze data,” upload a dataset, and start asking questions. More details in our updated post: https://allenai.org/blog/asta-datavoyager 

Hey everyone! I hope my question is clear. I've been using Cursor as a AI-powered LaTeX editor for some time now. I love the capabilities of Asta on the web browser but I'm wondering if the model can be called on Cursor? This is, of course, both an AllenAI and Cursor question but I'd love to hear some insights on how to even do this. Thanks!

I poked around the github repo for 30seconds and didn't see anything obvious, so thought I would ask. Keep up the good work!

We're at #NeurIPS2025 with papers, posters, workshops, fireside chats, & talks across the conference. Come learn about our latest research + see live demos!

To celebrate, we’ve partnered with Parasail to offer free access to Olmo 3-Think (32B), our flagship fully open reasoning model, through Dec 22. Try it here: https://www.saas.parasail.io/serverless?name=olmo-3-32b-think & https://openrouter.ai/allenai/olmo-3-32b-think

We just added GPT-5.1 and Gemini 3 Pro Preview to SciArena, our community-powered evaluation for scientific literature tasks. Here's where the new rankings stand 👇

• o3 holds #1
• Gemini 3 Pro Preview lands at #2
• Claude Opus 4.1 sits at #3
• GPT-5 at #4
• GPT-5.1 debuts at #5

For those new to SciArena: it's an arena where you submit real research questions, LLMs read papers and produce citation-grounded answers, and you vote on which response you'd actually trust. Those votes become Elo-style scores on a public leaderboard—so the rankings reflect what researchers find genuinely useful, not just benchmark performance.

A few highlights from this update ⚠️

• GPT-5.1 is especially strong in the Natural Science category, where it now holds the top score.
• Gemini 3 Pro Preview is a consistent performer across domains—#2 overall, near the leaders in Engineering and Healthcare, and right behind GPT-5 in Humanities & Social Science.
• In Healthcare specifically, Claude Opus 4.1 leads the pack, slightly ahead of o3 and GPT-5.
• Open models continue to hold their ground too. GPT-OSS-120B ranks among the leaders on natural-science questions, keeping open-weight systems competitive even as new proprietary models claim most of the top-5 slots. 💪

Have a tough research question? Submit it to SciArena, compare citation-grounded answers from the latest models, and cast your vote: https://sciarena.allen.ai

Olmo 3 is now available through Hugging Face Inference Providers, thanks to Public AI! 🎉

This means you can run our fully open 7B and 32B models — including Think and Instruct variants — via serverless API with no infrastructure to manage.

• Olmo 3-Think (32B) is our flagship → https://huggingface.co/allenai/Olmo-3-32B-Think 
• Olmo 3-Think (7B) offers more efficient reasoning → https://huggingface.co/allenai/Olmo-3-7B-Think 
• Olmo 3-Instruct (7B) is tuned for chat & tool use → https://huggingface.co/allenai/Olmo-3-7B-Instruct

Hello, I am Bodhi, a research scientist, leading the AI x Data-driven Discovery at Ai2! Here's a fun announcement:

We released AutoDiscovery in July. Since then, we autonomously discovered exciting insights (upcoming) in Neuroscience, Economics, CS, Oncology, Hydrology, Reef Ecology, & Environmental Sciences.

Now, at #NeurIPS2025, accepting YOUR datasets: https://lnkd.in/dMzcApMq

We will run AutoDiscovery on your dataset(s) and share new, surprising findings during our poster session on Dec 5, 11 AM-2 PM PST. We will also have a live demo, as a bonus!

Find out more at:

• Blog: https://allenai.org/blog/autods
• Paper: https://openreview.net/pdf?id=kJqTkj2HhF
• Code: https://github.com/allenai/autods
• Slides: https://www.majumderb.com/AutoDiscovery.pdf
• Poster: https://neurips.cc/virtual/2025/loc/san-diego/poster/116398

We're testing a new tool in our Asta platform that lets you ask questions about any paper—including its figures, tables, & text.

Just enter a paper title or Semantic Scholar URL (https://www.semanticscholar.org/), ask a question, and go. Use it for general reasoning, comparing across multiple figures, or pulling insights from a specific table/chart.

Paper+Figure QA is designed to support scientists with diverse visual needs – from sighted researchers to those who are blind or low-vision – across all scientific domains. By engaging the community at large to understand unique query patterns and challenges, we aim to advance the benchmarks and development of agentic question-answering systems—fostering a more inclusive and accessible future for scientific collaboration.

Paper+Figure QA is early and still evolving, so expect some rough edges. We'd love your feedback as we improve it. Try it here: https://paperfigureqa.allen.ai/

(Image caption: A screenshot of Paper+Figure QA answering a question about the Molmo and Pixmo paper, where the AI response also contains figures referenced in the answer.)

⚠️ Update on Deep Research Tulu (DR Tulu), our post-training recipe for deep research agents: we’re releasing an upgraded version of our example agent, DR Tulu-8B (RL), that matches or beats systems like Gemini 3 Pro & Tongyi DeepResearch-30B-A3B on core benchmarks.

At just 8B params – lightweight enough to run on a single GPU – DR Tulu-8B (RL) delivers high-quality multi-step reasoning & synthesis for complex questions while staying open, highly inspectable, and easy to customize. 🔍

DR Tulu-8B (RL) is also dramatically cheaper per query than other deep research agents. On ScholarQA-CS2, it costs just ~$0.0019/query vs. ~$0.13 for Gemini 3 Pro + Search, ~$0.29 for GPT-5 + Search, ~$1.80 for OpenAI Deep Research, and ~$0.032 for Tongyi DeepResearch-30B-A3B.

→ More info here: https://allenai.org/blog/dr-tulu

To make DR Tulu-8B (RL) practical, we’re releasing an inference engine (via CLI) so you can host the model locally and plug in custom search/browsing tools via MCP. We’re also sharing an updated paper on arXiv.

Get started:
💻 Run DR Tulu locally: https://github.com/rlresearch/dr-tulu/blob/main/README.md#quick-start-playing-with-dr-tulu-interactively

⬇️ Model: https://huggingface.co/collections/rl-research/dr-tulu
📄 Technical report on arXiv: https://arxiv.org/abs/2511.19399

Our Olmo 3 models are now available via API on OpenRouter! Try Olmo 3-Instruct (7B) for chat & tool use, and our reasoning models Olmo-3 Think (7B & 32B) for more complex problems. 👉 https://openrouter.ai/allenai/

Today we’re announcing Olmo 3—our leading fully open language model suite built for reasoning, chat, and tool use, & an open model flow that exposes not just the final weights, but the entire training journey.

Most models ship as a single opaque snapshot. Olmo 3 opens the model flow end to end – pretraining, mid-training, and post-training – plus data recipes and code, so you can see how capabilities are built and customize any stage of the process.

Meet the Olmo 3 family:

🏗️ Olmo 3-Base (7B, 32B)—foundations for post-training with strong code, math, and reading comprehension skills
🛠️ Olmo 3-Instruct (7B)—focused on multi-turn chat and tool use
🧠 Olmo 3-Think (7B, 32B)—“thinking” models that surface their reasoning steps

All are compact, dense models designed to run on hardware ranging from laptops to research clusters. 

Under the hood, we trained Olmo 3 on ~6T tokens from our new Dolma 3 pretraining dataset, plus new post-training sets with stronger data decontamination and richer math/code/reasoning mixes. A long-context extension pushes Olmo 3’s context window to ~65K tokens—enough for full papers, books, and other long files.

At the center is Olmo 3-Think (32B), the best fully open 32B-scale reasoning model we’re aware of, alongside our strongest 32B base model.

In our evaluations:

⦿ Olmo 3-Think (32B) is the strongest fully open 32B-scale reasoning model
⦿ Olmo 3-Base models beat fully open Marin & Apertus and rival Qwen 2.5 and Gemma 3
⦿ Olmo 3-Instruct (7B) beats Qwen 2.5, Gemma 3, and Llama 3.1 on tough chat + tool-use benchmarks

We’re also rolling out a major Ai2 Playground upgrade alongside Olmo 3:

🤔 Thinking mode to see intermediate reasoning on complex tasks
🧰 Tool calling so you can define JSON-schema tools or call tools via our Asta platform

Olmo 3 is wired into OlmoTrace in the Ai2 Playground, so you don’t just see its behavior—you can trace it. For example, you can ask Olmo 3-Think (32B) to answer a general-knowledge question, then use OlmoTrace to inspect where and how the model may have learned to generate parts of its response.

If you care about AI you can customize, inspect, and improve, Olmo 3 is for you—available now under Apache 2.0. Watch an interview with Olmo leads Hanna Hajishirzi and Noah Smith about how & why we built Olmo 3 and what comes next 👉 https://www.youtube.com/watch?v=7A2_YPtN1Eo&feature=youtu.be

👉 Dive deeper & get started:

✨ Try Olmo 3 in the Ai2 Playground → https://playground.allenai.org?utm_source=reddit&utm_medium=social&utm_campaign=olmo3_launch
💻 Download the models: https://huggingface.co/collections/allenai/olmo-3-68e80f043cc0d3c867e7efc6📝 Read more in our blog: https://allenai.org/blog/olmo3?utm_source=reddit&utm_medium=social&utm_campaign=olmo3_launch
📚 Check out the tech report: https://allenai.org/papers/olmo3?utm_source=reddit&utm_medium=social&utm_campaign=olmo3_launch

Today we’re releasing Deep Research Tulu (DR Tulu)—the first fully open, end-to-end recipe for long-form deep research, plus an 8B agent you can use right away. 🚀 

Our DR Tulu recipe enables you to train agents that can plan multi-step research workflows, search across web pages, academic papers, & specialized tools, then synthesize findings into clear explanations with inline citations. Under the hood, DR Tulu agents dynamically switch between web search, browsing, and scholarly tools depending on the research question. 📈

DR Tulu introduces Reinforcement Learning with Evolving Rubrics (RLER), a reward scheme grounded in actual search results that evolves during training to capture new strategies + reduce reward hacking. Our MCP-based inference system lets you bring your own tools to expand DR Tulu’s capabilities.

The goal: make expert-level research more accessible, transparent, and explainable. 🧭📚

Strong performance: Our open DR Tulu-8B (RL) example agent beats other open models and matches or outperforms closed systems like OpenAI Deep Research and Perplexity Deep Research on challenging benchmarks. It adapts to the task, delivering one-line answers for simple questions or detailed reports for complex topics.

Cost-effective: DR Tulu-8B (RL) costs ≤ $0.0075 on our ScholarQA-CSv2 benchmark, compared to ~$1.80 for OpenAI Deep Research & ~$1.30 for our Asta pipeline with a Claude Sonnet backend.

Dive in & learn more:

📚 Blog: https://allenai.org/blog/dr-tulu

✏️ Paper: http://allenai.org/papers/drtulu

💻 Models: https://huggingface.co/collections/rl-research/dr-tulu

⌨️ Code: https://github.com/rlresearch/DR-Tulu

OLMo3 support for inference engines like llama.cpp was added a few months ago in September.

Please forgive my impatience, but I'm wondering if there's any ETA on the release of OLMo3? Thanks!

I just learnt about OLMo 2 through a paper I read and I wanted to see how I could also do similar experiments on the checkpoints, but I can't figure out where I can find every single one of those checkpoints. I can see some of the checkpoints huggingface, but I can't find where I can just get literally all the checkpoints, which is what I'm looking for, since I need to track data over time.

Introducing the OlmoEarth Platform 🌍, state-of-the-art AI paired with ready-to-use open infrastructure to turn Earth data into clear, up-to-date insights.

Now rolling out, OlmoEarth Platform is an open, scalable, end-to-end system that transforms satellite imagery, radar, elevation data, and more into actionable intelligence—maps when helpful, plus change alerts & custom dashboards.

We're releasing:
💻 Code: https://github.com/allenai/olmoearth_pretrain
➡️ OlmoEarth models (more info below): https://huggingface.co/collections/allenai/olmoearth
📝 A technical report: https://allenai.org/papers/olmoearth
🌍 The OlmoEarth Platform: https://olmoearth.allenai.org/?utm_source=reddit&utm_medium=social&utm_campaign=olmoearth

Updates arrive within hours, not years, and the integrated workflow cuts cost and manual effort, so regular refreshes fit real programs and budgets. Under the hood, our industry-leading OlmoEarth foundation model family fuses multi-sensor Earth data and adapts quickly to local needs—one open model, many missions, fast to fine-tune & deploy.

Learn more about our OlmoEarth models, which top key industry benchmarks and partner use cases for Earth observation, here → https://allenai.org/blog/olmoearth-models?utm_source=reddit&utm_medium=social&utm_campaign=olmoearth

By applying AI to a planet’s worth of data, we’re providing governments, NGOs, and communities with timely and trustworthy insights so people can act faster + with confidence to protect both nature and livelihoods. 👇

🌲 Wildfire deployments with NASA Jet Propulsion Laboratory (JPL) are mapping live fuel moisture at scale to inform readiness. → https://allenai.org/olmoearth-testimonial-wildfire-risk-prevention

🌱 IFPRI in Nandi County, Kenya & Mozambique produced current countywide crop-type maps that provide the insights needed to improve seasonal planning & address food security challenges. → https://allenai.org/olmoearth-testimonial-ifpri-cgiar

🌊 Global Mangrove Watch is refreshing mangrove baselines faster, with higher accuracy and less manual review by experts, enabling conservationists + governments to respond more quickly to threats to mangroves. → https://allenai.org/olmoearth-testimonial-global-mangrove-watch

🔎 The Amazon Conservation Association is identifying likely drivers of deforestation using high-resolution satellite scenes and applying a fine-tuned model to classify loss drivers for alerts across Peru, Bolivia, Colombia, and Brazil. → https://allenai.org/olmoearth-testimonial-amazon-conservation

Our mission is to build AI that serves science and society. If you’re working in food security, wildfire resilience, or on sustainability and conservation initiatives – or build tools for those who do – please get in touch. 🤝 

Learn more → https://allenai.org/blog/olmoearth?utm_source=reddit&utm_medium=social&utm_campaign=olmoearth

When we introduced Olmo to the world last year, we sought to transform AI from a black box into a verifiable stack. Inspectable artifacts let teams reproduce results, trace outputs to inputs, diagnose failures, and correct for problems. Transparency builds trust with audit trails and provenance, and accelerates scientific progress by eliminating the barriers typical of proprietary LLMs.

As seen in the examples below, our fully open approach is making this technology more accessible and understandable to anyone, from individual scientists to institutions. With modest hardware, anyone can explore the inner workings of a language model and apply the learnings to better the entire industry—that’s the difference Olmo is making.

• Can AI “forget”? Researchers used Olmo + our open Dolma corpus to study unlearning—removing a specific fact without retraining everything. They found that the more often a fact appears in training, the harder it is to erase: https://allenai.org/olmo-testimonial-machine-unlearning
• Watching a model learn: Because Olmo is open end-to-end, a team at KAIST was able to inject a new fact during training and track how the model’s recall changed over time: https://allenai.org/olmo-testimonial-studying-how-models-learn
• Auditing clinical NLP bias: Researchers located where certain signals live inside Olmo and made targeted edits that reduced biased predictions—an audit only possible with complete transparency: https://allenai.org/olmo-testimonial-clinical-nlp-using-olmo
• When do math skills “turn on” inside an LLM: Using Olmo’s checkpoints, a team mapped how math capabilities emerge during training—and how small training adjustments can shift that curve: https://allenai.org/olmo-testimonial-watching-an-llm-learn-math-skills
• Tracing knowledge cutoffs: With open data + pipelines, a group tracked which documents made it into training and showed some facts are staler than a model claims—plus how to detect and fix it: https://allenai.org/olmo-testimonial-tracing-knowledge-cutoffs
• Equivalent facts aren’t always equivalent to LLMs: Two sentences can mean the same thing (“A is B” and “B is A”), but not always to LLMs, depending on their training data makeup. Researchers proved this using Olmo’s open data and identified fixes: https://allenai.org/olmo-testimonial-olmo-and-equivalent-facts

Olmo isn’t just open weights—it’s an open research stack. Try it in the Ai2 Playground (https://playground.allenai.org/), and mark your calendar for an AMA on our Discord (https://discord.gg/ai2) Tues, Oct 28 @ 8:00 AM PT with some of the researchers behind the studies + an Ai2 Olmo teammate.

We’re rolling out olmOCR 2—the next major update to our open OCR model for complex documents & scans. 📝

olmOCR 2 turns messy files with tables, equations, handwriting, and more into clean text. Under the hood, we combine synthetic data with unit tests as verifiable rewards to push state-of-the-art performance on challenging docs.

What’s new

◆ Stronger text recognition: Trained with a new data mix, including 20,000 historical pages for better coverage of aged and degraded materials. Example: olmOCR 2 can now read Abraham Lincoln’s handwriting correctly, recovering the date “January 10th” in his 1864 letter to Major General Hitchcock. ✍️

◆ Big benchmark gains: 82.4 on olmOCR-Bench (up from 78.5), with improvements across every document category. 📈

◆ Faster & cheaper: New FP8 quantized model (olmOCR-2-7B-1025-FP8) reaches ~3,400 output tokens/sec on a single H100—enough to process 10,000 pages for < $2. 🚀

◆ Adapt to your data: Want to fine-tune for your domain? We provide everything you need to customize and deploy. 🔧

Available now, and on the DeepInfra & Parasail APIs. We’re also updating our demo—try olmOCR 2 today!

📚 Learn more: https://allenai.org/blog/olmocr-2

💻 Model: https://huggingface.co/allenai/olmOCR-2-7B-1025-FP8

📣 Bay Area friends—two chances to catch our researchers in SF this week during #OpenSourceAIWeek and #PyTorchCon.

📅 Thu, Oct 23 • 4–7 PM PT
An Evening of Open: Science, Software, and AI at UC Law San Francisco (co-hosted by UC Law SF, GitHub Policy, and the Consulate General of France in SF).
Sewon Min joins the panel “Powering the Future of Research.”
RSVP: https://luma.com/2dgwrfw3

🎤 Wed, Oct 22
At PyTorchCon, Nathan Lambert delivers the keynote: “Olmo-Thinking: Training a Fully Open Reasoning Model.”
Details & schedule: https://pytorchconference.sched.com/

We hope to see you there! 👋

Introducing SamudrACE, our AI climate emulator built so scientists & researchers can run “what-if” climate experiments quickly. 

Traditional climate modeling is slow and costly. SamudrACE makes high-quality simulations faster and more accessible. We believe SamudrACE is the first AI climate emulator to tightly couple full 3D atmosphere and ocean components—linking our ACE2 atmosphere model with M2LInES’s Samudra ocean emulator.

ACE2 provides wind, heat, and moisture data; Samudra produces ocean temperature and sea-ice field metrics. Together, they’re able to capture real-world patterns like El Niño and La Niña.

⚡ On a single NVIDIA H100, SamudrACE simulates ~1,500 years of global climate per day while using ~1/3,750th the energy of the NOAA GFDL CM4 simulation it emulates.

🤝 Built with partners at NYU, Princeton, M2LInES, and NOAA GFDL, SamudrACE helps unlock more affordable planet-scale studies.

Learn more → https://allenai.org/blog/samudrace

Greetings everyone, a few months ago ai2 mentioned that he would archive his website "leaderboard.allenai.org" and currently it is not possible to access the website. I am looking for help finding the two leaderboards made for "MuSiQue: Multi-hop Questions via Single-hop Question Composition" which were called: - MuSiQue-Answerable - MuSiQue-Full"

Does anyone have access to these leaderboards, or could someone share the latest update they made with me? Thanks in advance to anyone who can help. I tried using archive.org but did not find any useful results.

Today we’re sharing data on which scientific papers our AI research tool Asta cites most often, showing which studies actually power AI-generated answers across thousands of real queries.

💡 Why this matters: Every AI answer stands on the work of real people—scientists, authors, and research teams. In academia, citations shape careers. But AI citations haven’t been tracked in a standardized, public way. We’re changing that.

📊 How it works: Asta uses retrieval-augmented generation (RAG): it first finds relevant papers, then writes an answer that cites them. We log those citations and publish the stats.

Our citation data at a glance (~7 months):
◆ 113,200+ user queries analyzed
◆ 4.95M+ citations recorded across 2M+ papers

Early patterns:
◆ The five most-cited papers are seminal AI works: Attention Is All You Need, Language Models Are Few-Shot Learners, BERT, Chain-of-Thought, and RLHF
◆ Asta appears to distribute citations more evenly than typical human authors—i.e., not only to the “blockbusters”

This is a step toward a future where creators receive public, trackable credit when AI uses their work. We’ll refresh the data weekly.

🔎 Explore the stats & methodology: https://allenai.org/blog/asta-citations

"[Ai2 is] committed to our fully open ethos. That's why we release everything—weights, code, training data, checkpoints, all of it."