File size: 2,268 Bytes
b7b71e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ---
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}
}
```
|