| --- |
| pretty_name: Open-MOPD Data |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - open-mopd |
| - math |
| - code |
| - instruction-following |
| - reinforcement-learning |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: rl_prompt_mix |
| data_files: rl_prompt_mix/train.parquet |
| - config_name: sft_openr1_math_93k |
| data_files: sft/openr1_math_93k/train.parquet |
| - config_name: sft_ocr_50k |
| data_files: sft/ocr_50k/train-00000.parquet |
| - config_name: sft_instruction_nemotron_aligned |
| data_files: sft/instruction_nemotron_aligned/*.parquet |
| - config_name: eval_aime24 |
| data_files: eval/math/aime24.parquet |
| - config_name: eval_aime25 |
| data_files: eval/math/aime25.parquet |
| - config_name: eval_livecodebench_v5 |
| data_files: eval/code/livecodebench_v5.parquet |
| - config_name: eval_livecodebench_v6 |
| data_files: eval/code/livecodebench_v6.parquet |
| - config_name: eval_ifeval_aligned |
| data_files: eval/if/ifeval_aligned.parquet |
| - config_name: eval_ifbench_test_aligned |
| data_files: eval/if/ifbench_test_aligned.parquet |
| --- |
| |
| # Open-MOPD Data |
|
|
| This repository contains the training and evaluation data released with |
| Open-MOPD, including mixed-domain supervised fine-tuning data, the shared |
| RL/OPD prompt mixture, and six evaluation benchmarks. |
|
|
| ## Dataset contents |
|
|
| | Configuration | Description | Examples | |
| |---|---|---:| |
| | `rl_prompt_mix` | Shared math, code, and instruction-following prompts for RL and OPD | 86,931 | |
| | `sft_openr1_math_93k` | Math SFT data in a unified think-tag format | 93,733 | |
| | `sft_ocr_50k` | Sampled OpenCodeReasoning data in a unified format | 50,000 | |
| | `sft_instruction_nemotron_aligned` | Instruction-following SFT data in a unified format | 820,039 | |
| | `eval_aime24`, `eval_aime25` | Math evaluation sets | 30 + 30 | |
| | `eval_livecodebench_v5`, `eval_livecodebench_v6` | Code evaluation sets | 167 + 175 | |
| | `eval_ifeval_aligned`, `eval_ifbench_test_aligned` | Instruction-following evaluation sets | 541 + 300 | |
|
|
| The three SFT domains are balanced by response-token count rather than example |
| count. After balancing, math, code, and instruction following contribute |
| approximately 37.3%, 28.1%, and 34.6% of training response tokens. |
|
|
| `rl_prompt_mix/manifest.json` records the construction and decontamination of |
| the shared prompt mixture. In particular, code training prompts explicitly |
| exclude LiveCodeBench. |
|
|
| ## Evaluation protocol |
|
|
| Metrics are averaged per dataset, then per domain, followed by a macro-average |
| across the three domains. |
|
|
| - Math: AIME24 and AIME25, avg@64, temperature 0.6. |
| - Code: LiveCodeBench v5 and v6, avg@10, temperature 1.0. |
| - Instruction following: IFEval and IFBench_test, `n=1`, temperature 1.0, |
| with `enable_thinking=true`. |
|
|
| All evaluations use `max_model_len=32768`, `top_p=0.95`, `top_k=-1`, and |
| `stop_token_ids=[128012]`. Sampling-related columns preserved in the Parquet |
| files are legacy construction metadata and do not define the final protocol; |
| use the settings documented above. |
|
|
| ## Related models |
|
|
| - `BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-Final` |
| - `BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT` |
| - `BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math` |
| - `BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Code` |
| - `BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-IF` |
|
|
| ## Sources and licensing |
|
|
| The release is derived from OpenR1-Math, OpenCodeReasoning, |
| Instruction-Nemotron, and the listed public evaluation benchmarks. Users must |
| follow the licenses and terms of the corresponding upstream sources when using |
| or redistributing each configuration. |
|
|
