llonara, Camille Challier, Clément Charmillot, Hanna Yukhymenko
Vision RL: Mukhammadali Sayfiddinov, Badr Al Mahouri, Joshua Oyebanji, Alessandro Bombarda
QA Gym: Oscar Wohlfahrt, Florian Kolly, Nicolas Cuveillier, Marko Stojanovic, Valentina Pyatkin
Tool Gym: Loïc Deslarzes, Michal Tešnar, Francesco Monti, Morgane Magnin, Dana Shayakhmetova, Suei-Wen Chen
IF Gym: Emma Anastassova, Mattea Busato, Taliesin Perez, Simon Pierre, Valentina Pyatkin
Alignment: Davit Melikidze, Marian Schneider, Simon Matrenok, Thomas Kleine Buening, Barna Pasztor, Vinzenz Thoma, Jessica Lam
Applications and Inference: Robert Smith, Aryan Ahadinia, Oleg Lavrovsky, Davit Melikidze, Ahmad Fraij, Helen El Khoury, Szabina Horváth-Mikulas, Burak Sezen, Xiaozhe Yao
Framework Integrations: Raphael Kreft, Anunay Yadav, Aryan Ahadinia, Robert Smith
Evaluations and Safety: Yannick Metz, Frederike Lübeck, Davit Melikidze, Alexander Sternfeld, Vinzenz Thoma, Vilém Zouhar, Hanna Yukhymenko, Jannis Vamvas, Andrei Kucharavy, Michelle Wastl, Arthur Wuhrmann
Interpretability: Anna Hedström, Rita Sevastjanova, Arundhati Balasubramaniam, Tymoteusz Kwiecinski, Lorenzo Baggi, Marco De Negri, Luca Sartori, Moritz Reihs, Lasse B. Strand, Thilo Spinner
Infrastructure: Fabian Bösch, Jonathan Coles, Lukas Drescher, David Isztl, Miguel Gila, Sebastian Keller, Mark Klein, Henrique Mendonça, Fawzi Roberto Mohamed, Gennaro Oliva, Elia Palme, Marco Passerini, Tim Robinson, Stefano Schuppli, Joost VandeVondele
Management and Advisors: Sean F. Carroll, Joost VandeVondele, Thomas Schulthess, Elliott Ash, Andreas Krause, Ana Klimovic, Martin Rajman, Torsten Hoefler, Alexander Ilic, Mennatallah El-Assady, Timo Kehrer
Project Leadership: Imanol Schlag, Martin Jaggi, Antoine Bosselut
Appendix B Vision Datasets by Training Stage
The stagewise tables give the vision pool of each stage: for every dataset, the prepared samples and input tokens available to the run. The mix a run trains on, that is, the tokens trained on including repetition, appears per modality in Table 3 for the pretraining stages, per dataset in Appendix F for the long-context extension, and per source in Table 8 for long-context SFT. Stages 1 and 2 appear in Table B.1 and Table B.2, the cooldown and long-context pretraining pools in Table B.3 and Table B.4, and the SFT and long-context SFT pools in Table B.5 and Table B.6. Table B.2 and Table B.3 report the Apertus 1.5 8B pools. The Apertus 1.5 70B cooldown additionally reuses datasets already listed for Stage 2 in Table B.2, which we do not repeat. Vision reinforcement learning adds 50,000 in-house BlindTasks-RL prompts, which Table 10 reports separately. The preference-optimization stages use no multimodal inputs. The SFT tables here report the production pools; the smaller image pool of the capability-filtering SFT phase appears in Table 7, whereas the audio pool is the same in both phases.
Table B.1: Vision pool of Stage 1.
Dataset
Samples
Tokens
Dataset
Samples
Tokens
BigDocs-7.5M
2.2M
6.1B
MedTrinity-25M
4.4M
2.0B
BigEarthNet
24.0K
6.0M
MINT-1T ArXiv
4.6M
14.4B
BUSI
732
934.7K
MINT-1T HTML
125.0M
228.0B
COVID-US
9.7K
11.1M
MINT-1T PDF
58.8M
93.4B
DDTI
347
278.6K
MAmmoTH-VL
1.5M
3.1B
DFC2020
5.1K
1.5M
Open UFI
700
3.7M
Handwriting documents
15.0K
49.9M
OpenPMC-18M
17.3M
4.8B
Handwriting slides
2.5K
4.3M
PMC OA
1.3M
364.9M
HoloAssist
266.6K
126.6M
RFMiD 2
754
2.4M
ISIC
216.3K
18.2M
SCIN
10.4K
31.6M
LAION Aesthetics 12M
4.4M
8.7B
SLID-E
3.0K
5.3M
MedMax
446.0K
537.8M
SWISSIMAGE 10 cm
1.3M
5.1B
MedMNIST
236.6K
52.3M
TreeOfLife-10M
765.5K
4.4B
Table B.2: Vision pool of Stage 2.
Dataset
Samples
Tokens
Dataset
Samples
Tokens
Ultrasound Liver Images
735
1.8M
MedTrinity-25M
6.9M
5.1B
Art Museums PD-440K
10.6M
26.7B
Megalith-10M
8.9M
26.4B
BLIP3 Grounding 50M
33.7M
47.5B
MIT-10M
11.8M
30.5B
BLIP3o Long Caption
29.3M
36.9B
Molmo2-MultiImageQA
564.3K
2.6B
Brain Tumor MRI
7.2K
6.7M
MultiCare Case Images
96.7K
106.8M
Breast Ultrasound (BUSI)
773
1.3M
MultiCare Images
161.6K
132.