OpenEuroLLM 9B multilingual math RLVR, Phase A step 32 (experimental)

This is a full BF16 export after 32 additional DAPO/RLVR optimizer updates from oellm-9b-math-rlvr-canary-step16-experimental. The earlier ancestor is the Neonkraft instruct-SFT checkpoint at revision 85bf18fb4f0bee6ac6270f06b1d1c6b3be200f31. This is an experimental math-training example, not an official OpenEuroLLM release or a production-ready model. It was trained with TMAX/Open-Instruct, not Verl. The model.safetensors file is 18,203,942,400 bytes; SHA-256: 7f9ad2b9783263b516cdfbff338be21862c526e50e4a1a96ab9bf95d7097f4be.

What was trained

Slurm marked one auxiliary inner step failed during setup and another cancelled during teardown, although the top-level job completed 0:0, all 32 learner updates finished, and the step-32 model and restart state were written. The inner-step anomaly has not been root-caused; this release is a training result, not a claim of flawless orchestration.

The source EU component is 2,944 difficulty-1-to-3 problems: 128 per each of 23 non-English EU official languages. The English replay is the original 256-row canary training pool, not the larger 2,048-row English pool listed in the data builder's manifest. The control configuration intended an 80/20 final mix, but the pinned TMAX backend interprets each factor as the fraction to retain from its own file. Its job log records 2,355 EU and 51 English transformed rows. This is an important limitation of the experiment; it was not an 80/20 final training mixture. Both exact source training files are bundled as training_data/eu-math.parquet and training_data/english-pilot.parquet. Their SHA-256 values are 2c48c91df99cd1116fafd8f1f96f3ad26000711ebac5e3d3d925cfd66fc49eb6 and ec33cc0fcbd6be73d87ed8f0582683cc6f2e4e56297ffa7db92f3712a5ec3aff. The source dataset is birgermoell/oellm-math-rlvr at revision 0ffc9d6dc82717c25733b3172f4dbd63e48bab68.

The exact rendered job config, Slurm script and selection manifest are in repro/. Their original LUMI SHA-256 values are recorded in repro/SHA256SUMS. The YAML contains allocation-specific absolute paths: replace paths and account for a different cluster, or use the bundled Parquets and published starting checkpoint. The original run used a LUMI AI Factory ROCm container named lumi-multitorch-full-u24r70f21m50t210-20260807_115122.sif. The EU pool excludes the earlier canary train and selection holdout by semantic group; the full Phase A mixture deliberately replays the canary train set. The frozen multilingual evaluation set excludes the full Phase A train set. Never place ground_truth or verifier metadata in model-visible prompts.

Paired evaluations

Both comparisons use the same frozen prompts and deterministic generation protocol before and after Phase A: native chat template, temperature 0, one sample per prompt, 1,024 response-token limit, 3,072 model-token limit, seed 20260930. The dataset, chat-template and selected-ID hashes matched for each pair. Correctness is numeric-answer equivalence under the project's lightweight verifier; this is not a proof-grade judge.

On the multilingual set, 27 prompts changed wrong-to-right and 14 right-to-wrong; exact two-sided McNemar p=0.0596. Non-English EU prompts improved in aggregate, while English changed by only one of 144 prompts. Think-tag use remained 100%, with zero unbalanced tags; reasoning-channel form rose 75.20% → 77.15%, and length stops fell 7.03% → 6.45%. The DAPO diagnostic remained near its floor, with no demonstrated improvement (p=0.511). Summary-only comparison artifacts are in eval/; held-out prompts and answers are not republished here.

These results support a bounded proof of a working math RL training pipeline, not a general claim that this model is better at mathematics. Many prompts are procedural, each non-English language has only 16 held-out examples, and we have not measured safety, broad reasoning, coding, agentic behavior, or long-context retention. The original architecture supports long context, but this RL run trained with a 3,072-token packing limit; do not infer 256K RL performance. Read the promotion plan before attempting a harder phase or deployment.

Minimal inference

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "birgermoell/oellm-9b-math-rlvr-phase-a-32step-experimental"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")
messages = [{"role": "user", "content": "What is 37 * 48? Answer in \\boxed{}."}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True,
                                       return_tensors="pt").to(model.device)
output = model.generate(inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(output[0, inputs.shape[-1]:], skip_special_tokens=False))

License and attribution

Apache-2.0, following the parent model and the math-source dataset. The SFT ancestor was trained on Dolci-Instruct-SFT, whose separate data terms and attribution are described on its model card. This personal-account release does not imply endorsement by the OpenEuroLLM project. The run used the LUMI supercomputer hosted by CSC in Finland.