OpenEuroLLM 9B math-RL canary, step 16 (experimental)
This is the 16-update starting checkpoint for the separate
oellm-9b-math-rlvr-phase-a-32step-experimental
release. It is a full BF16 model export, not an adapter. It starts from
Neonkraft/oellm-9b-256k-theta64m-prelude-anneal300b-instruct-sft
at revision 85bf18fb4f0bee6ac6270f06b1d1c6b3be200f31.
This is research evidence that a small, verifier-backed RL run can complete on LUMI. It is not
an official OpenEuroLLM release, a general math benchmark winner, or evidence of safety or
256K-context quality. It was trained with TMAX/Open-Instruct DAPO, not Verl.
The model.safetensors file is 18,203,942,400 bytes; SHA-256:
c5b01a4b13d5bd76fdb4a69e73994e73704bb73e62a6a067068032f46a3f7565.
Training
- Source:
birgermoell/oellm-math-rlvrat revision0ffc9d6dc82717c25733b3172f4dbd63e48bab68. - Training pool: 256 English difficulty-1-to-3 problems. The exact selected Parquet is
training_data/english-pilot.parquet, SHA-256ec33cc0fcbd6be73d87ed8f0582683cc6f2e4e56297ffa7db92f3712a5ec3aff. - Algorithm: DAPO with outcome-verifiable math reward; 16 optimizer updates, eight unique prompts by eight stochastic samples per update, temperature 1.0, active sampling.
- Optimizer: constant learning rate
5e-7,beta=0, BF16, DeepSpeed ZeRO-3, gradient checkpointing; seed 42. - Topology: two LUMI-G nodes, eight MI250X GCDs for the learner and eight for rollout
engines. Job
22398668completed in 14m30s, about 3.87 allocated GCD-hours. - Exact control/config:
BirgerMoell/oellm-rlvrandrepro/canary-run.yaml(SHA-256c55f686197dbbee6961a3f2f734b488a9379f566e21c54fb7116c001864f0974). Backend:OpenEuroLLM/tmax-reproductionat3f80d37042402b8363f39c9535723b0d4cb8de54.
The YAML records LUMI-specific absolute paths and container image. To rerun, replace the
project account, checkpoint and data paths for your allocation while preserving the recorded
data hashes and training parameters. ground_truth and verifier metadata must never be
included in model-visible prompts.
Evidence and limits
All 16 updates had finite, nonzero gradients; 128/128 accepted prompt groups had mixed
rewards, and a cold restart from saved learner state completed an additional update. On a
frozen 1,024-prompt English procedural holdout, greedy exact-answer accuracy was 42.97%
for the SFT parent and 44.63% for this canary (+1.66 percentage points; paired bootstrap
95% interval +0.20 to +3.13 points). This holdout is drawn from the same procedural
generator collection; step 16 was selected after inspecting step-8 and step-16
results, so the interval is exploratory rather than an untouched confirmation.
This is not an independent general-math estimate. See the
canary qualification record.
The model can still be wrong, overfit procedural templates, or produce unbalanced or unsound reasoning. Think-tag structure is not proof of correct reasoning. No safety, code, agentic, or long-context retention evaluation is claimed here.
License and attribution
Apache-2.0, following the parent model and the math-source dataset. The SFT parent was trained on Dolci-Instruct-SFT, whose separate data terms and attribution are described on its model card. OpenEuroLLM and LUMI/CSC contributors are acknowledged; this personal-account release does not imply project endorsement.