datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Gargantua-R1-Compact
Gargantua-R1 Distribution
Gargantua-R1-Compact(experimental purpose)
Gargantua-R1-Compact is a large-scale, high-quality reasoning dataset primarily designed for mathematical reasoning and STEM education. It contains approximately 6.67 million problems and solution traces, with a strong emphasis on mathematics (over 70%), as well as coverage of scientific domains, algorithmic challenges, and creative logic puzzles. The dataset is suitable for training and evaluating… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Gargantua-R1-Compact.Gargantua-R1-Compact
Gargantua-R1 Distribution
Gargantua-R1-Compact(experimental purpose)
Gargantua-R1-Compact is a large-scale, high-quality reasoning dataset primarily designed for mathematical reasoning and STEM education. It contains approximately 6.67 million problems and solution traces, with a strong emphasis on mathematics (over 70%), as well as coverage of scientific domains, algorithmic challenges, and creative logic puzzles. The dataset is suitable for training and evaluating… See the full description on the dataset page: https://huggingface.co/datasets/introvoyz041/Gargantua-R1-Compact.compactionbench-lme-text-subset
CompactionBench LME Text Subset
20 questions from LongMemEval-V2 filtered for text-answerable content.
Each task has ~160k tokens of web agent trajectory context (thoughts, actions, page states).
All answers are confirmed present in the text context.
Used for testing context compaction in long-running agents.
Format
JSONL with one task per line. Each line is a CompactionBench TaskRow with:
task_id
context (~160k tokens)
question
gold_answer
metadata (domain… See the full description on the dataset page: https://huggingface.co/datasets/Ayushnangia/compactionbench-lme-text-subset.