AdarshSingh7647/Eklav-Math-CotGen-Data
HETU-MathReasoning-CotGen-Data Training data for the HETU (Hints Enable True Understanding) paper. Task: math reasoning (AIME, GSM8K, MATH-500, Omni-MATH, GPQA-Diamond, MMLU) Method: CotGen Examples: 3,481 train / 35-36 held-out val Format: ShareGPT (system + conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory. Single-turn ShareGPT conversations, curated DeepSeek-R1-style math reasoning distillation. Each row: the raw math/logic problem (human turn) and the… See the full description on the dataset page: https://huggingface.co/datasets/AdarshSingh7647/Eklav-Math-CotGen-Data.
HETU-MathReasoning-CotGen-Data
Training data for the HETU (Hints Enable True Understanding) paper.
- Task: math reasoning (AIME, GSM8K, MATH-500, Omni-MATH, GPQA-Diamond, MMLU)
- Method: CotGen
- Examples: 3,481 train / 35-36 held-out val
- Format: ShareGPT (
system+conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory.
Single-turn ShareGPT conversations, curated DeepSeek-R1-style math reasoning distillation. Each row: the raw math/logic problem (human turn) and the full teacher chain-of-thought plus a boxed final answer (gpt turn), with loss computed over the entire gpt turn.
Files:
train.json-- training splitval.json-- held-out validation split
See the HETU paper for full dataset construction methodology, and the corresponding HETU-*-MathReasoning-CotGen model repos for checkpoints trained on this data.