.8
36.4
36.7
27.7
TruthfulQA
60.9
59.5
59.1
55.0
49.5
CommonsenseQA
75.3
74.7
74.9
74.4
74.5
SQuADv2
23.3
25.9
27.2
13.6
16.5
HellaSwag
69.9
73.6
70.0
75.8
73.5
BBH
67.5
67.1
67.4
67.0
64.7
ACPBench
35.8
43.0
35.6
32.3
27.8
DROP
51.3
50.7
51.4
50.1
48.5
Instruction Following and Chat
IFEval
85.0
85.0
83.9
80.8
78.2
Multi-IF
81.0
82.2
81.9
78.8
74.0
AlpacaEval
41.1
49.4
38.6
49.9
20.4
Math
GSM8K
80.7
81.9
81.0
81.3
73.5
MATH
34.0
33.7
31.5
31.1
27.4
Minerva MATH
35.0
31.2
34.3
32.6
31.1
MathQA
46.5
47.3
46.7
46.9
45.9
MATH-500
33.8
30.4
34.6
30.4
31.6
AIME 24
6.7
0.0
0.0
6.7
0.0
AIME 25
13.3
6.7
6.7
0.0
10.0
Coding
HumanEval
86.1
85.0
83.5
85.5
84.1
MBPP
49.8
48.2
50.2
49.2
47.4
Multilingual and Cultural
Global-MMLU
62.4
60.3
59.7
60.0
57.9
MGSM
76.4
80.8
79.6
77.6
79.2
Multilingual TruthfulQA
54.1
53.4
53.4
51.1
48.4
INCLUDE-44
58.1
57.3
57.1
57.4
56.7
INCLUDE-45
38.8
38.4
37.8
40.1
39.6
XNLI
43.6
44.3
44.3
46.2
46.2
BLEnD
66.8
66.5
66.5
65.9
65.6
CulturalBench
72.8
73.5
73.0
74.0
73.9
SwitzerlandQA
64.7
63.9
64.1
64.8
64.5
Safety and Alignment
BBQ
67.6
66.5
66.6
64.2
63.5
ToxiGen
83.8
82.8
83.1
82.6
79.6
WMDP
53.6
53.5
54.0
52.5
51.1
Development Average
55.5
55.4
54.6
54.0
51.4
Held-out Knowledge and Reasoning
AGIEval
47.8
46.1
46.8
39.4
38.4
ARC-Challenge
84.4
84.8
84.5
81.8
77.6
GPQA
31.0
32.4
25.7
29.5
29.0
Held-out Math
GSM8K-Platinum
80.6
81.7
79.7
78.3
79.7
Held-out Multilingual and Cultural
mLogiQA
37.6
34.9
34.4
37.8
34.8
Multilingual ARC
41.7
40.9
41.0
38.1
36.5
Held-out Average
53.8
53.5
52.0
50.8
49.3
J.1.2 Apertus 1.5 70B
Transferring the Apertus 1.5 8B hyperparameters.
We test the transfer on Apertus 1.0 70B SFT, the SFT checkpoint of swiss-ai/Apertus-70B-Instruct-2509 (Table J.6). The best Apertus 1.5 8B online setting (learning rate 5⋅10−65{\cdot}10^{-6}, β=0.1\beta{=}0.1) does not transfer to this scale: it shortens responses from 280 to 160 tokens on average, and coding (44.3 vs. 60.5) and math (20.7 vs. 27.2) fall below the SFT checkpoint. A lower learning rate with a larger β\beta (2⋅10−62{\cdot}10^{-6} with β∈{0.25,0.35}\beta\in\{0.25,0.35\}) avoids this degradation and raises the overall average from 46.3 to 48.9–49.1.
Table J.6: Online DPO from Apertus 1.0 70B SFT with the learning rate / β\beta given in the headers, sorted by average; 5⋅10−65{\cdot}10^{-6} / 0.1 is the Apertus 1.5 8B setting, and the rightmost column is the SFT checkpoint. Scores in percent (higher is better) with thinking disabled; for degeneration, lower is better. Bold marks the best DPO value per skill-group and average row. Each skill-group row is the mean over the benchmarks within that group, and the overall average is the mean over all individual benchmarks.
Online DPO (Learning Rate / β\beta)
Skill Group
2⋅10−62{\cdot}10^{-6} / 0.25
2⋅10−62{\cdot}10^{-6} / 0.35
5⋅10−65{\cdot}10^{-6} / 0.1
SFT
Knowledge
58.0
57.0
60.3
54.2
Reasoning
54.4
54.6
53.0
50.1
Instruction Following
76.8
75.7
79.7
68.9
Chat
10.7
9.2
15.7
6.4
Math
28.7
29.3
20.7
27.2
Coding
60.0
61.0
44.3
60.5
Multilingual
55.7
56.2
52.5
55.8
Cultural Knowledge
68.8
68.1
67.8
68.7
Safety
67.6
67.9
70.0
57.8
Charter Alignment
32.2
27.4
50.8
21.9
Tool Use
40.8
42.9
42.4
45.0
Overall Average
49.1
48.9
47.6
46.3
Mean response length (tokens)
245.9
252.9
160.0
279.9
Degeneration (%)
0.6
2.3
0.0
8.4
Offline and mixture.
The RL checkpoint of Apertus 1.5 70B produces much shorter responses than its SFT checkpoint (253 vs. 367 tokens; Table L.3). Offline DPO from the RL checkpoint reverses this shortening and raises the mean response length from 253 to 501 tokens (Table J.7). From the RL checkpoint, offline DPO alone reaches an average of 56.1, above the mixture (55.6) and online DPO (55.5). The final Apertus 1.5 70B recipe runs offline DPO followed by online DPO (Section 4.6), which restores the response length before on-policy training.
Table J.7: Offline DPO, mixture, and online DPO from the Apertus 1.5 70B RL checkpoint, sorted by average; the rightmost column is the RL checkp