MarinSkyRL-Open-MOPD-SmolLM3-3B-step-32

MarinSkyRL Open-MOPD student, step 32

This is a MarinSkyRL-native Open-MOPD student after 32 optimizer steps. It starts from the authors' mixed-domain SFT checkpoint. Student responses were scored by the authors' math, code, and instruction-following RL teachers, routed by domain. The objective uses the student's selected top-16 token IDs and a clipped policy surrogate. This is an early checkpoint, not the authors' step-200 final model.

The checkpoint is an unquantized, six-file Hugging Face export of the durable MarinSkyRL global_step_32 FSDP2 checkpoint. The policy export was used for the independent step-32 evaluation. The export's model.safetensors SHA-256 is bb7326640142069bc2e1fba5f54f15e0cccb1ff861f34f318b372eaab7abaf4b. The original trainer-state SHA-256 is a3f50a849ed43f06deef731bc676a1bf8d31fcf5d34ed2f63c8b6aa1c985da34. The training source is MarinSkyRL commit 12e6da9e.

The retained training and inline-evaluation traces cover the original, fast-gate, and fast-full run segments. They include student responses and teacher-route metadata, but not the historical per-token teacher scoring arrays. The local open-mopd-repro/open-mopd-reproduce.md artifact gives the exact source, input, and launch sequence.

These are three of the six released target benchmarks. LiveCodeBench v5/v6 and IFBench were generated, but paper-comparable scores are not claimed here. The training run reached a durable step-34 checkpoint and was then stopped by the campaign owner. This model is the earlier evaluated step-32 export. See the Open-MOPD paper for the authors' full experiment and final results.

Use AutoTokenizer.from_pretrained and AutoModelForCausalLM.from_pretrained with this repository ID. The export contains config.json, generation_config.json, tokenizer.json, tokenizer_config.json, and chat_template.jinja with the weights. For comparable AIME results, use the released 30-question sets with 64 samples per question and the recorded evaluation prompts, generation limits, and grader. A single greedy answer is not comparable to mean@64.

We release our tools to the public with the intent that they are used safely, ethically, and responsibly. They are meant to contribute to the public's understanding of science, and aid in its advancement. By using this Marin model, you agree to comply with all applicable law.