BytedTsinghua-SIA/Open-MOPD-Data
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… See the full description on the dataset page: https://huggingface.co/datasets/BytedTsinghua-SIA/Open-MOPD-Data.
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
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 IFBenchtest, `n=1`, temperature 1.0, with `enablethinking=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-FinalBytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFTBytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-MathBytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-CodeBytedTsinghua-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.