OctoThinker/MegaMath-Web-Pro-Max
OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling The Curation of MegaMath-Web-Pro-Max Step 1: Uniformly and randomly sample millions of documents from the MegaMath-Web corpus, stratified by publication year; Step 2: Annotate them using Llama-3.1-70B-instruct with a scoring prompt from FineMath and prepare the seed data; Step 3: Training a fasttext carefully with proper preprocessing; Step 4: Filtering documents with a threshold (i.e., 0.4); Step 5:… See the full description on the dataset page: https://huggingface.co/datasets/OctoThinker/MegaMath-Web-Pro-Max.
| Author / organization | OctoThinker |
|---|---|
| License (as declared) | odc-by |
| Downloads | 2.3K (as reported by Hugging Face) |
| Likes | 41 |
| Created | 25 June 2025 |
| Last updated at source | 6 July 2025 |
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