Datasets:
File size: 2,321 Bytes
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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: finance
data_files: data/finance.jsonl
- config_name: if
data_files: data/if.jsonl
- config_name: tau2
data_files: data/tau2.jsonl
---
# domain-mopd-data
Prompts of the five domains for multi-teacher on-policy distillation (MOPD and IM-MOPD) 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` | 8,384 | four- and five-option MedQA training prompts |
| `law` | 31,654 | CaseHOLD and bar-exam prompts; the 91 prompts longer than 15,872 tokens are skipped during training, leaving 31,563 |
| `finance` | 7,565 | the Finance RL prompts ([MMOPD/domain-rl-data](https://huggingface.co/datasets/MMOPD/domain-rl-data)) |
| `if` | 11,728 | the IF RL prompts ([MMOPD/domain-rl-data](https://huggingface.co/datasets/MMOPD/domain-rl-data)) |
| `tau2` | 31,718 | dialogue contexts from the retail, airline and telecom portions of `inclusionAI/AReaL-tau2-data`, each the prompt for the next assistant turn |
## Format
One JSON object per line.
- `medical`, `law`, `finance`, `if`: `input` = user prompt, `output` = reference answer (answer letter / number; empty for `if`), `meta` = source information (for `if`: the verifiable instructions `instruction_id_list` and `kwargs`).
- `tau2`: `prompt_text` = the dialogue context rendered with the Qwen3 chat template (tool schemas included), ending with the generation prompt of the next assistant turn; `sub_domain` = retail / airline / telecom.
## Sources
MedQA, CaseHOLD, [reglab/barexam_qa](https://huggingface.co/datasets/reglab/barexam_qa), FinQA, TAT-QA, nvidia/Nemotron-RL-Ultra-Training-Blends, 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}
}
```
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