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.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/finance.jsonl filter=lfs diff=lfs merge=lfs -text
+data/if.jsonl filter=lfs diff=lfs merge=lfs -text
+data/law.jsonl filter=lfs diff=lfs merge=lfs -text
+data/tau2.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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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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