| --- |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - mmopd |
| configs: |
| - config_name: medical |
| data_files: data/medical.jsonl |
| - config_name: law |
| data_files: data/law.jsonl |
| - config_name: tau2 |
| data_files: data/tau2.jsonl |
| --- |
| |
| # domain-sft-data |
|
|
| SFT data of the three SFT domain teachers (Medical, Law, Tool Use) of [No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation](https://arxiv.org/abs/2609.34745) ([code](https://github.com/shkim0824/immopd)). |
|
|
| | Subset | Rows | Content | |
| |---|---|---| |
| | `medical` | 40,853 | correct, deduplicated samples generated by Qwen3.6-35B-A3B from MedQA training prompts ([MMOPD/Qwen3-4B-OT3-medical](https://huggingface.co/MMOPD/Qwen3-4B-OT3-medical)) | |
| | `law` | 56,123 | correct, deduplicated samples generated by Qwen3.6-35B-A3B from CaseHOLD and bar-exam prompts ([MMOPD/Qwen3-4B-OT3-law](https://huggingface.co/MMOPD/Qwen3-4B-OT3-law)) | |
| | `tau2` | 32,758 | retail, airline and telecom samples from `inclusionAI/AReaL-tau2-data`, after removing overlength samples and telecom task-ID overlap ([MMOPD/Qwen3-4B-OT3-tau2](https://huggingface.co/MMOPD/Qwen3-4B-OT3-tau2)) | |
|
|
| ## Format |
|
|
| One JSON object per line. |
|
|
| - `medical`, `law`: `messages` = [user, assistant]; the assistant turn is `<think> {reasoning}\n</think>\n\n{answer}` (OpenThoughts3 thinking opening). `meta` = prompt id, source and selection statistics. |
| - `tau2`: `prompt_text` = the dialogue context rendered with the Qwen3 chat template and the sub-domain tool schemas; `completion` = the target assistant turn (same thinking format; supervision covers this turn only, earlier thinking is removed from the context); `sub_domain` = retail / airline / telecom. |
|
|
| ## Sources |
|
|
| MedQA, CaseHOLD, [reglab/barexam_qa](https://huggingface.co/datasets/reglab/barexam_qa), [inclusionAI/AReaL-tau2-data](https://huggingface.co/datasets/inclusionAI/AReaL-tau2-data). Please follow the licenses of the source datasets. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{kim2026immopd, |
| title = {No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation}, |
| author = {Kim, Seonghyeon and Jang, Chaeyun and Lee, Noah and Kim, Boseop and Lee, Juho}, |
| journal = {arXiv preprint arXiv:2609.34745}, |
| year = {2026} |
| } |
| ``` |
|
|
