Apriel-1.5-15b-Thinker Article Swipe
Shruthan Radhakrishna
,
Anurag Tiwari
,
A. P. SHUKLA
,
Masoud Hashemi
,
Rishabh Maheshwary
,
Shiva Krishna Reddy Malay
,
Jash Mehta
,
Pulkit Pattnaik
,
Saloni Mittal
,
Khalil Slimi
,
Kelechi Ogueji
,
Akintunde Oladipo
,
Soham Parikh
,
Oluwanifemi Bamgbose
,
Toby Liang
,
Ahmed Masry
,
Khyati Mahajan
,
Sai Rajeswar Mudumba
,
Vikas Yadav
,
Sathwik Tejaswi Madhusudhan
,
Torsten Scholak
,
Sagar Davasam
,
Srinivas Sunkara
,
Nicholas Chapados
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2510.01141
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2510.01141
We present Apriel-1.5-15B-Thinker, a 15-billion parameter open-weights multimodal reasoning model that achieves frontier-level performance through training design rather than sheer scale. Starting from Pixtral-12B, we apply a progressive three-stage methodology: (1) depth upscaling to expand reasoning capacity without pretraining from scratch, (2) staged continual pre-training that first develops foundational text and vision understanding, then enhances visual reasoning through targeted synthetic data generation addressing spatial structure, compositional understanding, and fine-grained perception, and (3) high-quality text-only supervised fine-tuning on curated instruction-response pairs with explicit reasoning traces spanning mathematics, coding, science, and tool use. Notably, our model achieves competitive results without reinforcement learning or preference optimization, isolating the contribution of our data-centric continual pre-training approach. On the Artificial Analysis Intelligence Index, Apriel-1.5-15B-Thinker attains a score of 52, matching DeepSeek-R1-0528 despite requiring significantly fewer computational resources. Across ten image benchmarks, its performance is on average within five points of Gemini-2.5-Flash and Claude Sonnet-3.7, a key achievement for a model operating within single-GPU deployment constraints. Our results demonstrate that thoughtful mid-training 2 design can close substantial capability gaps without massive scale, making frontier-level multimodal reasoning accessible to organizations with limited infrastructure. We release the model checkpoint, all training recipes, and evaluation protocols under the MIT license to to advance open-source research.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2510.01141
- https://arxiv.org/pdf/2510.01141
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414810693
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4414810693Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2510.01141Digital Object Identifier
- Title
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Apriel-1.5-15b-ThinkerWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-10-01Full publication date if available
- Authors
-
Shruthan Radhakrishna, Anurag Tiwari, A. P. SHUKLA, Masoud Hashemi, Rishabh Maheshwary, Shiva Krishna Reddy Malay, Jash Mehta, Pulkit Pattnaik, Saloni Mittal, Khalil Slimi, Kelechi Ogueji, Akintunde Oladipo, Soham Parikh, Oluwanifemi Bamgbose, Toby Liang, Ahmed Masry, Khyati Mahajan, Sai Rajeswar Mudumba, Vikas Yadav, Sathwik Tejaswi Madhusudhan, Torsten Scholak, Sagar Davasam, Srinivas Sunkara, Nicholas ChapadosList of authors in order
- Landing page
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https://arxiv.org/abs/2510.01141Publisher landing page
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https://arxiv.org/pdf/2510.01141Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2510.01141Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.Intelligence | 116 |
| abstract_inverted_index.Pixtral-12B, | 23 |
| abstract_inverted_index.constraints. | 159 |
| abstract_inverted_index.contribution | 105 |
| abstract_inverted_index.data-centric | 108 |
| abstract_inverted_index.fine-grained | 68 |
| abstract_inverted_index.foundational | 48 |
| abstract_inverted_index.high-quality | 72 |
| abstract_inverted_index.mathematics, | 85 |
| abstract_inverted_index.methodology: | 29 |
| abstract_inverted_index.mid-training | 165 |
| abstract_inverted_index.open-weights | 6 |
| abstract_inverted_index.pre-training | 44, 110 |
| abstract_inverted_index.compositional | 65 |
| abstract_inverted_index.computational | 130 |
| abstract_inverted_index.optimization, | 102 |
| abstract_inverted_index.organizations | 182 |
| abstract_inverted_index.reinforcement | 98 |
| abstract_inverted_index.significantly | 128 |
| abstract_inverted_index.frontier-level | 12, 177 |
| abstract_inverted_index.understanding, | 52, 66 |
| abstract_inverted_index.infrastructure. | 185 |
| abstract_inverted_index.DeepSeek-R1-0528 | 125 |
| abstract_inverted_index.Gemini-2.5-Flash | 145 |
| abstract_inverted_index.instruction-response | 78 |
| abstract_inverted_index.Apriel-1.5-15B-Thinker | 118 |
| abstract_inverted_index.Apriel-1.5-15B-Thinker, | 2 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 24 |
| citation_normalized_percentile |