| --- |
| 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} |
| } |
| ``` |
|
|
