Vision
MMMU
–
–
–
+7.3
−-1.4
MMMU-Pro
–
–
–
+7.8
−-1.2
MMStar
–
–
–
+18.2
−-4.7
MathVision
–
–
–
−-4.1
−-2.7
ScienceQA
–
–
–
+7.1
−-2.8
BLINK
–
–
–
+7.8
−-3.7
VisualPuzzles
–
–
–
+0.9
−-1.7
AI2D
–
–
–
+4.5
−-2.6
ChartQA
–
–
–
+7.4
−-1.9
DocVQA
–
–
–
−-2.5
−-0.4
TextVQA
–
–
–
−-4.0
−-0.5
RealWorldQA
–
–
–
+5.9
−-4.5
POPE
–
–
–
+0.7
−-3.4
OCRBench
–
–
–
−-3.5
+0.2
Vision average (14 tasks)
–
–
–
+3.8
−-2.2
H.2 SFT Data Decontamination Details
We report the detailed count of removed samples after decontamination for the text SFT mixture in Table H.2.
Table H.2: Decontamination statistics for the SFT data sources. For each dataset we report the example count before decontamination (Original), the number of examples removed by matching against evaluation benchmarks (Removed), the resulting count (Final), and the fraction removed. We compute removals per source prior to mixing.
Dataset
Original
Removed
Final
% Removed
OLMo2 OASST1
7,125
97
7,028
1.36%
Llama-Nemotron Chat
20,853
264
20,589
1.27%
Llama-Nemotron Safety
13,797
31
13,766
0.22%
EuroBlocks Aya
2,117
0
2,117
0.00%
EuroBlocks Refusal
7,648
0
7,648
0.00%
EuroBlocks Low-Resource
7,702
0
7,702
0.00%
EuroBlocks Multilingual (2512)a
346,751
60,367
286,384
17.41%
SmolTalk2 Multilingual (8 lang)
219,223
47
219,176
0.02%
SmolTalk2 (everyday)
2,259
0
2,259
0.00%
SmolTalk2 (magpie)
264,257
2,019
262,238
0.76%
SmolTalk2 (rewrite)
48,093
1
48,092
0.00%
SmolTalk2 (summarize)
92,728
16
92,712
0.02%
SmolTalk2 (system-chats)
29,473
0
29,473
0.00%
Swiss German Dialects
2,674
0
2,674
0.00%
Romansh Varieties
46,078
0
46,078
0.00%
African Languages
8,730
0
8,730
0.00%
WikiQA
482,799
0
482,799
0.00%
German→\rightarrowRomansh MT
30,000
0
30,000
0.00%
Swiss Charter Q&A
88
0
88
0.00%
Glaive Function Calling v2
52,732
0
52,732
0.00%
GSM8K (TIR)
7,473
0
7,473
0.00%
IF-mix (combined)b
201,481
13,455
188,026
6.68%
Table-GPT
13,222
0
13,222
0.00%
FeTaQA
10,330
0
10,330
0.00%
SciRIFF
72,367
3,338
69,029
4.61%
RiddleSense
4,531
0
4,531
0.00%
Medical-o1
13,696
311
13,385
2.27%
NuminaMath 1.5
417,101
4,908
412,193
1.18%
DeepMath-103K
103,022
0
103,022
0.00%
HARP
4,542
0
4,542
0.00%
OpenMathReasoning
2,362,555
55,490
2,307,065
2.35%
OpenCodeReasoning v2
529,210
16,782
512,428
3.17%
OpenMathReasoning (CoT)
688,801
22,412
666,389
3.25%
Total
6,113,458
179,538
5,933,920
2.94%
a False positives, common for multilingual data, dominate the high removal rate.
b Removals stem largely from IFEval-style instructions and also include false positives.
Appendix I Preference Alignment Data
This appendix documents the preference data of Section 4.6: the rubric prompts we give the judge, the composition of the prompt set, the pool of response models, and the distribution of the resulting scores. The released artifacts are swiss-ai/Apertus-v1.5-Preference-Data for the preference pairs and swiss-ai/Apertus-v1.5-Preference-Pool for the underlying annotated completions.
I.1 Judge Rubric Prompts
Qwen3-235B-A22B-Instruct-2507 (Yang et al., 2025a) rates each completion on the four aspects independently, one request per aspect, with the system prompt of Section I.1 and the aspect prompt as the user message. We make annotation deterministic and disable thinking: the judge decodes at temperature 0 and generates exactly one token, constrained to the token identifiers of the digits one through five, and we keep the renormalized distribution over those five tokens, so an aspect score is the expected value ∑kk​pk\sum_{k}k\,p_{k} and is continuous on [1,5][1,5]. The final score of a completion is the mean over the four aspects. Across the full matrix of 6,235,908 scored completions the four aspects correlate between 0.838 and 0.916, so the mean is close to a single latent quality axis; Section J.2 takes up the consequence for the online stage, which scores on helpfulness alone.
Judge System Prompt
You are an impartial judge. Your role is to critically evaluate the quality of an AI assistant response based on a given criteria. You’ll recei