datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ResearchMath-14k
ResearchMath-14k
ResearchMath-14k is a collection of 14,056 research-level mathematical problem records extracted from papers, open-problem lists, workshop sheets, and related academic sources. Each record contains the original extracted question, a rewritten self-contained problem statement, taxonomy labels, and open-status metadata.
Paper: ResearchMath-14K: Scaling Research-Level Mathematics via Agents
Load
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/amphora/ResearchMath-14k.math-intuition-reasoning-traces
math-intuition reasoning traces
Full chain-of-thought traces from 7 reasoning models on the same 4,020 problems, graded
by each problem family's own verifier.
Questions come from
amphora/math-intuition-20260908-402-easy-10
— 402 arXiv-derived problem families x 10 seeds, easy preset. Every row here refers to an id
in that dataset, so prompts and the instance cache can be joined from it.
Generation settings
Identical for every model, so the traces are directly… See the full description on the dataset page: https://huggingface.co/datasets/amphora/math-intuition-reasoning-traces.LessWrong-Amplify-Instruct
This is the Official LessWrong-Amplify-Instruct dataset. Over 500 multi-turn examples, and many more coming soon!
This leverages Amplify-Instruct method to extend thousands of scraped Less-Wrong posts into advanced in-depth multi-turn conversations.
Comprised of over 500 highly filtered multi-turn synthetic conversations.
Average context length per conversation is over 2,000 tokens. (will measure this more accurately soon)
Synthetically created using a newly developed pipeline… See the full description on the dataset page: https://huggingface.co/datasets/LDJnr/LessWrong-Amplify-Instruct.