of 100 random hits. For EuroBlocks 2512, almost all of the removed samples are unrelated non-contaminated multilingual samples. For our instruction-following mix, the majority of “flagged” samples contain an instruction format similar to some from the IFEval benchmark, which does not necessarily mean direct contamination. We still decide to keep the original similarity threshold of 0.5 (as used in Apertus 1.0) to ensure we catch contaminated samples at the cost of false positives, which is still not critical. We remove contaminated samples by identifier. For image data, text-based matching does not transfer at all: templated prompts dominate, so nearly every n-gram hit is a false positive, and matching must happen at the image level instead. We therefore match the perceptual hashes of the training images (Section 2.2.1) against roughly 950,000 benchmark images; this catches near-duplicates of natural images but produces false positives in low-entropy domains such as satellite and medical imagery, which require manual thresholding. We provide full details with counts of dropped samples for every dataset from the text SFT mixture in Section H.2 (Table H.2).
4.2.1 Image
The image pool used to train the auxiliary checkpoint for capability filtering of text data spans 20 datasets with 17,534,672 sequences and roughly 58.1B tokens; Table 7 lists them. We assemble the pool under the zero-reasoning policy of that checkpoint’s training: we exclude or strip sources whose assistant turns carry reasoning markup, and for template-heavy sources we regenerate the assistant answers with Qwen3.6-27B and thinking disabled. The permissively licensed subset of LLaVA-OneVision-1.5 (An et al., 2025) and SenseNova-SI-8M (Cai et al., 2026) together hold roughly 80% of the tokens in Table 7; the remainder covers document, chart, and table understanding; captioning and open question answering; GUI pointing; and domain imagery from remote sensing, pathology, and memes. The production phase additionally trains on reasoning-bearing image data: Innovator-VL (Wen et al., 2026) ships with the boundaries of its thinking spans stripped by its creators, so we regenerate the reasoning traces by reformatting the samples with Qwen3.6-27B and keep only outputs whose answers match the original with a ROUGE-L score above 0.9; in the pool of Table B.5, roughly 2.7 million of the dataset’s 24.8 million samples carry restored reasoning. We remove structured answer tags from source datasets, so that all supervision matches the Apertus chat format; the processing of the image SFT datasets otherwise follows the multimodal data pipeline of Section 2.2. The share of text data packed into image-SFT batches matters little in ablations, with benchmark movements small and split between 2.5% and 15%. For three FineVision subsets we regenerate the assistant turns with Qwen3.6-27B: for LNQA we keep the questions and replace the template-like single-fact answers with image-grounded answers of two to four sentences, and for emo-visual-data and Memotion 2 we replace the original Chinese descriptions and sentiment labels with two-turn English explanations of each meme, dropping samples that the generator flags as unsafe. In the Apertus 1.5 8B production run, text makes up 13.6% of the sequences and 7.8% of the tokens. Besides Innovator-VL, the production image pool carries thinking spans in Nemotron-Image-Training-v3 (22% of sequences), SenseNova-SI-8M (5%), and RSRCC (all sequences).
The production candidate pool contains 57,130,672 sequences and 157.8B input tokens. After we create the training and development splits and complete final packing, the pool of the Apertus 1.5 8B run contains 43,915,058 sequences and 143.4B input tokens, and Table B.5 gives its per-dataset composition.
Table 7: Image pool used to train the auxiliary checkpoint for capability filtering of text data: 20 datasets, 17.5M sequences, 58.1B tokens. The production SFT pool appears in Table B.5.
Dataset
Sequences
