---
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.