---

license: apache-2.0 base_model: allenai/Olmo-3.1-32B-Instruct-DPO language:

• en library_name: transformers datasets:
• allenai/Dolci-Instruct-RL

---

Model Details

Model Card for Olmo-3.1-32B-Instruct

We introduce Olmo 3, a new family of 7B and 32B models both Instruct and Think variants. Long chain-of-thought thinking improves reasoning tasks like math and coding.

Olmo is a series of Open language models designed to enable the science of language models. These models are pre-trained on the Dolma 3 dataset and post-trained on the Dolci datasets. We are releasing all code, checkpoints, logs (coming soon), and associated training details.

The core models released in this batch include the following:

Stage	Olmo 3 7B Think	Olmo (3/3.1) 32B Think	Olmo 3 7B Instruct	Olmo 3.1 32B Instruct
Base Model	Olmo-3-7B	Olmo-3-32B	Olmo-3-7B	Olmo-3-32B
SFT	Olmo-3-7B-Think-SFT	Olmo-3-32B-Think-SFT	Olmo-3-7B-Instruct-SFT	Olmo-3.1-32B-Instruct-SFT
DPO	Olmo-3-7B-Think-DPO	Olmo-3-32B-Think-DPO	Olmo-3-7B-Instruct-DPO	Olmo-3.1-32B-Instruct-DPO
Final Models (RLVR)	Olmo-3-7B-Think	Olmo-3-32B-Think Olmo-3.1-32B-Think	Olmo-3-7B-Instruct	Olmo-3.1-32B-Instruct

Model Description

• Developed by: Allen Institute for AI (Ai2)
• Model type: a Transformer style autoregressive language model.
• Language(s) (NLP): English
• License: This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.
• Contact: Technical inquiries: olmo@allenai.org. Press: press@allenai.org
• Date cutoff: Dec. 2024.

Model Sources

• Project Page: https://allenai.org/olmo
• Repositories:
  • Open-Instruct for DPO and RLVR: https://github.com/allenai/open-instruct
  • OLMo-Core for pre-training and SFT: https://github.com/allenai/OLMo-core
  • OLMo-Eval for evaluation: https://github.com/allenai/OLMo-Eval
• Paper:: https://allenai.org/papers/olmo3

Evaluation

Metric	Olmo 3.1 32B Instruct SFT	Olmo 3.1 32B Instruct DPO	Olmo 3.1 32B Instruct	Apertus 70B	Qwen 3 32B (No Think)	Qwen 3 VL 32B Instruct	Qwen 2.5 32B	Gemma 3 27B	Gemma 2 27B	OLMo 2 32B
Math
MATH	74.4	86.6	93.4	36.2	84.3	95.1	80.2	87.4	51.5	49.2
AIME 2024	12.7	35.2	67.8	0.31	27.9	75.4	15.7	28.9	4.7	4.6
AIME 2025	8.2	23.3	57.9	0.1	21.3	64.2	13.4	22.9	0.9	0.9
OMEGA	15.5	33.3	42.2	5.6	23.4	44.0	19.2	24.0	9.1	9.8
Reasoning
BigBenchHard	69.0	82.1	84.0	57.0	80.4	89.0	80.9	82.4	66.0	65.6
ZebraLogic	30.6	51.1	61.7	9.0	28.4	86.7	24.1	24.8	17.2	13.3
AGI Eval English	71.7	79.4	79.5	61.6	82.4	89.4	78.9	76.9	70.9	68.4
Coding
HumanEvalPlus	80.8	85.7	86.7	42.9	83.9	89.3	82.6	79.2	67.5	44.4
MBPP+	61.5	63.6	65.1	45.8	67.9	69.0	66.6	65.7	61.2	49.0
LiveCodeBench v3	35.4	49.6	54.7	9.7	57.5	70.2	49.9	39.0	28.7	10.6
IF
IFEval	87.7	87.3	88.8	70.4	87.5	88.1	81.9	85.4	62.1	85.8
IFBench	29.7	36.3	39.7	26.0	31.3	37.2	36.7	31.3	27.8	36.4
Knowledge & QA
MMLU	79.0	81.9	80.9	70.2	85.8	88.7	84.6	74.6	76.1	77.1
PopQA	23.7	28.5	25.0	33.5	25.9	25.7	28.0	30.2	30.4	37.2
GPQA	41.3	47.9	48.6	27.9	54.4	61.4	44.6	45.0	39.9	36.4
Chat
AlpacaEval 2 LC	42.2	69.7	59.8	19.9	67.9	84.3	81.9	65.5	39.8	38.0
Safety	92.1	88.9	89.5	77.1	81.6	85.8	82.2	68.8	74.4	84.2

Model Details

Stage 1: SFT

• supervised fine-tuning on the Dolci-Think-SFT-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
• Datasets: Dolci-Think-SFT-7B, Dolci-Instruct-SFT

Stage 2:DPO

• direct preference optimization on the Dolci-Think-DPO-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
• Datasets: Dolci-Think-DPO-7B, Dolci-Instruct-DPO

Stage 3: RLVR

• reinforcement learning from verifiable rewards on the Dolci-Think-RL-7B dataset. This dataset consits of math, code, instruction-following, and general chat queries.
• Datasets: Dolci-Think-RL-7B, Dolci-Instruct-RL

Bias, Risks, and Limitations

Like any base language model or fine-tuned model without safety filtering, these models can easily be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified.

License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

Citation

A technical manuscript is forthcoming!

Model Card Contact

For errors in this model card, contact olmo@allenai.org.