Apriel-1.5-15b-Thinker
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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Shruthan Radhakrishna
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Aman Tiwari
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2025-10-02T13:54:38.519Z
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Aman Tiwari
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amant555
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Aanjaneya Shukla
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Masoud Hashemi
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Rishabh Maheshwary
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Shiva Krishna Reddy Malay
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Jash Mehta
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Pulkit Pattnaik
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Saloni Mittal
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Khalil Slimi
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Kelechi Ogueji
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Akintunde Oladipo
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2025-10-13T10:10:36.940Z
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Akintunde Oladipo
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theyorubayesian
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Soham Parikh
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Oluwanifemi Bamgbose
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2025-10-22T15:47:00.117Z
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Nifemi Bamgbose
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onifemibam
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Toby Liang
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Ahmed Masry
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Khyati Mahajan
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Sai Rajeswar Mudumba
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2025-10-05T12:47:29.534Z
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Sai Rajeswar
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rajeswarsai
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Vikas Yadav
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Sathwik Tejaswi Madhusudhan
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Torsten Scholak
claimed_verified
2025-10-17T04:14:39.233Z
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Torsten Scholak
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tscholak
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Sagar Davasam
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Srinivas Sunkara
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Nicholas Chapados
Apriel-1.5-15b-Thinker
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.
A 15-billion parameter multimodal reasoning model achieves competitive performance through a progressive training methodology without reinforcement learning, demonstrating efficient use of computational resources.
multimodal reasoning model depth upscaling staged continual pre-training synthetic data generation spatial structure compositional understanding fine-grained perception text-only supervised fine-tuning reasoning traces Artificial Analysis Intelligence Index single-GPU deployment