del sizes, online DPO uses KL penalty coefficient β=0.1\beta=0.1, a linear schedule with a warmup ratio of 0.1, the AdamW optimizer (Loshchilov & Hutter, 2019) (β1=0.9\beta_{1}=0.9, β2=0.999\beta_{2}=0.999, ϵ=10−8\epsilon=10^{-8}) with a weight decay of 0.01, and prompt and response lengths of 2,048 tokens each. The prompt cap removes a further 3,210 prompts, so an online epoch covers 242,688 of them. We train for one epoch on our verl fork with FSDP (Zhao et al., 2023) and SGLang rollouts. Apertus 1.5 8B trains at a learning rate of 5×10−65\times 10^{-6} with 256 prompts per batch for 948 steps on 24 nodes, 16 for training and eight for judge annotation. Apertus 1.5 70B trains at a learning rate of 2.5×10−62.5\times 10^{-6} with 512 prompts per batch for 474 steps on 144 nodes, 128 for training and 16 for annotation. Both runs complete in under 12 hours. Table G.2 lists the full configurations.
Results.
Preference alignment raises the overall average of our development benchmark suite from 49.3% to 54.8% for Apertus 1.5 8B and from 55.3% to 61.2% for Apertus 1.5 70B. The gains are broad. Knowledge rises by 13.0 and 2.3 points at the two scales, mathematics by 4.1 and 4.8, reasoning by 2.6 and 2.7, and safety by 3.9 and 5.4, while cultural knowledge rises by 8.7 points for Apertus 1.5 70B. Chat improves by 8.7 and 25.9 points, tool use by 13.1 and 12.5, and charter alignment by 22.5 and 21.3. Degenerate outputs become rarer at both scales, falling from 1.7% to 0.6% and from 10.6% to 3.3%. The gains carry to thinking mode although no stage trains with thinking enabled: the thinking-mode average rises by 4.2 points for Apertus 1.5 8B and 6.6 for Apertus 1.5 70B. Table 26, Table 27, Table 28, and Section L.1 provide detailed evaluation results, discussed in Section 5.3.2.
4.6.2 Charter Alignment with SDPO
The final post-training stage aligns the model with the Apertus charter (Hernández-Cano et al., 2026) through text-only self-distillation policy optimization (SDPO) (Hübotter et al., 2026; Buening et al., 2026).
During training, the student samples a response to a prompt. A judge model receives that response together with the Apertus charter and writes textual feedback on how the response can be improved to follow the charter more closely. We form the teacher signal by calculating the log-probabilities of the initial student response with the textual feedback added to the original user prompt. Conditioning on the feedback in context shifts these log-probabilities toward a more charter-aligned version of the same response. We then distill the teacher log-probabilities back into the student, which sees the original prompt alone, with a top-kk reverse KL loss. The stage is deliberately short and lightweight, so that the capabilities built by the preceding stages carry over intact.
The two releases differ at this stage: Apertus 1.5 8B includes SDPO, whereas Apertus 1.5 70B ships the preference-aligned DPO checkpoint of Section 4.6. In ablations, initially SDPO regressed the capabilities of the larger model, most visibly on math capabilities, and we dropped the stage from the release for that reason. Investigating after the release, we traced the regression not to SDPO itself but to an inconsistent tokenizer configuration between the DPO and the SDPO runs. Training is stable once the configuration is fixed, though the gains remain modest next to those observed at 8B scale, increasing charter alignment by 2.4% while most other capabilities are unchanged, the exceptions being a 1.1% gain on coding and a drop of 1.2% on chat capabilities.
Data.
We train on value-laden safety prompts, where the charter shapes the response most. The prompts come from the gretelai/gretel-safety-alignment-en-v1 dataset, of which we keep the prompts alone and discard the pre-generated responses. Decontamination against our evaluation suite leaves 8,354 prompts. We compare this against Dolci-Instruct-DPO (Team Olmo et al., 2025), RealToxicityPrompts (Gehman e