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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

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01Cartinoe5930 /math_difficulty_datatextn<1K0 likes1.4k downloads6mo agoHugging Face02guanning-ai /dapo14k_difficultytext10K<n<100K0 likes664 downloads7mo agoHugging Face03mlfoundations-dev /math_stratos_scale_judged_and_annotated_with_difficultytabular100K<n<1M0 likes242 downloads2y agoHugging Face04a-m-team /AM-Math-Difficulty-RLFor more open-source datasets, models, and methodologies, please visit our GitHub repository. We believe that the selection of training data for reinforcement learning is crucial. To validate this, we conducted several experiments exploring how data difficulty influences training performance. Our data sources originate from numerous excellent open-source projects, and we sincerely appreciate their contributions, without which our current achievements would not have been possible.… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-Math-Difficulty-RL.texttext-generation100K<n<1M16 likes171 downloads1y agoHugging Face05stair-lab /reeval-difficulty-for-helmtext100K<n<1M1 likes149 downloads2y agoHugging Face06vibhuiitj /Exercise-Synthetic-split-ncert-chapter-mapped_filtered_difficulty_scoredtabular1M<n<10M0 likes144 downloads5mo agoHugging Face07JierunChen /MMMU_with_difficulty_level MMMU with difficulty level tags This dataset extends the 🤗 MMMU val benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs. 🚀 Data Usage from datasets import load_dataset dataset = load_dataset("JierunChen/MMMU_with_difficulty_level") print(dataset) 📑… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MMMU_with_difficulty_level.imagequestion-answeringn<1K0 likes131 downloads1y agoHugging Face08ronantakizawa /aozora-text-difficulty Aozora Text Difficulty Dataset This dataset contains Japanese literary texts from the Aozora Bunko digital library, enhanced with jReadability-based difficulty analysis for Japanese language learning and curriculum development. Dataset Overview Source: Aozora Bunko (青空文庫) - Japan's premier digital library of public domain literature Enhancement: jReadability-based difficulty scoring using research-backed Japanese readability models Primary Methodology: jReadability - A… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/aozora-text-difficulty.tabulartext-classification1K<n<10K1 likes102 downloads10mo agoHugging Face09CoffeeGitta /difficulty-E2H-AMC-generations Generations Dataset: E2H-AMC LLM-generated solutions across train/validation/test splits for multiple models. Columns Column Type Description problem str Problem statement generated_solutions list Generated solutions with scores success_rate float Fraction of correct generations majority_vote_is_correct int (0/1) Whether majority vote is correct k int Number of samples generated temperature float Sampling temperature max_len int Maximum… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-E2H-AMC-generations.tabular10K<n<100K0 likes99 downloads4mo agoHugging Face10taesiri /BlindLoop-Difficulty-Feedbackgated BlindLoop Difficulty Feedback This is the public, hash-bound release of BlindLoop Section 3. Coding agents generated executable visual-question tasks; each task's inverse program checked the answer from rendered pixels. For complete feedback transactions, the exact same five images were evaluated by three frontier VLMs and the resulting difficulty signal was returned to the next generation episode. Contents Config Unit Rows tasks generated task 266… See the full description on the dataset page: https://huggingface.co/datasets/taesiri/BlindLoop-Difficulty-Feedback.imagevisual-question-answering10K<n<100K0 likes93 downloads22d agoHugging Face11mlfoundations-dev /seed_math_exploit_difficulty_annotationtabular100K<n<1M0 likes88 downloads2y agoHugging Face12JierunChen /MathVision_with_difficulty_level MathVision with difficulty level tags This dataset extends the 🤗 MathVision benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs. 🚀 Data Usage from datasets import load_dataset dataset = load_dataset("JierunChen/MathVision_with_difficulty_level") print(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MathVision_with_difficulty_level.imagequestion-answering1K<n<10K0 likes85 downloads1y agoHugging Face13pe-nlp /Skywork-DAPOCL180-difficultytext10K<n<100K0 likes77 downloads1y agoHugging Face14junlinw /Qwen2.5-7B-Instruct-Turbo_labeled_numina_difficulty_162K_10text10K<n<100K0 likes71 downloads1y agoHugging Face15JierunChen /MathVista_with_difficulty_level MathVista with difficulty level tags This dataset extends the 🤗 MathVista testmini benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs. 🚀 Data Usage from datasets import load_dataset dataset = load_dataset("JierunChen/MathVista_with_difficulty_level")… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MathVista_with_difficulty_level.imagemultiple-choice1K<n<10K1 likes70 downloads1y agoHugging Face16CoffeeGitta /difficulty-gsm8k-generations Generations Dataset: gsm8k LLM-generated solutions across train/validation/test splits for multiple models. Columns Column Type Description problem str Problem statement generated_solutions list Generated solutions with scores success_rate float Fraction of correct generations majority_vote_is_correct int (0/1) Whether majority vote is correct k int Number of samples generated temperature float Sampling temperature max_len int Maximum… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-gsm8k-generations.tabular10K<n<100K0 likes70 downloads4mo agoHugging Face17CoffeeGitta /difficulty-aime_2025-generations Generations Dataset: aime_2025 Paper: LLMs Encode Their Failures: Predicting Success from Pre-Generation ActivationsCode: GitHub LLM-generated solutions across train/validation/test splits for multiple models. Columns Column Type Description problem str Problem statement generated_solutions list Generated solutions with scores success_rate float Fraction of correct generations majority_vote_is_correct int (0/1) Whether majority vote is correct k… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-aime_2025-generations.tabulartext-generationn<1K0 likes68 downloads1mo agoHugging Face18LLParallax /difficulty-eval-64tabularn<1K0 likes67 downloads2mo agoHugging Face19Mohamed-Abbas /cleaned-plotqa-v2-difficulty Cleaned-PlotQA v2 with difficulty tiers (vectorized + calibrated) This repository augments jrc/cleaned-plotqa-v2 by adding a single column difficulty_tier ∈ {easy, medium, hard} computed with a vectorized, batch‑scored rule set and cutoffs calibrated on a 1,000‑example sample to avoid tier collapse. Tier counts easy: 77403 medium: 78521 hard: 43369 total labeled: 199293 Notes Only one new column is added; original fields remain unchanged. The scoring runs… See the full description on the dataset page: https://huggingface.co/datasets/Mohamed-Abbas/cleaned-plotqa-v2-difficulty.image100K<n<1M0 likes64 downloads1y agoHugging Face20zhenghaoxu /R2E-Gym-Lite-with-Difficultytabular1K<n<10K5 likes62 downloads1y agoHugging Face21mlfoundations-dev /difficulty_filtering_seed_mathtabular10K<n<100K0 likes60 downloads2y agoHugging Face22Seungjun /Korean-DeepMath-with-Difficulty Korean DeepMath with Difficulty This dataset enriches ChuGyouk/Korean-DeepMath with difficulty and topic metadata from zwhe99/DeepMath-103K. Join procedure Rows are matched using Korean-DeepMath[extra_info][index] -> original DeepMath row index. Added fields original_index difficulty topic Intended use Prepared for controlled Korean mathematical reasoning SFT experiments, including difficulty-aware sampling such as TDCS. No Easy /… See the full description on the dataset page: https://huggingface.co/datasets/Seungjun/Korean-DeepMath-with-Difficulty.tabulartext-generation100K<n<1M0 likes51 downloads29d agoHugging Face23junlinw /Qwen2.5-7B-Instruct_numina_difficulty_162K_10_MATH-SA_15_50_0.4_15text1K<n<10K0 likes49 downloads1y agoHugging Face24shivank21 /merged_difficulty_small_finalimage1K<n<10K0 likes49 downloads1y agoHugging Face25CoffeeGitta /difficulty-MATH-generations Generations Dataset: MATH LLM-generated solutions across train/validation/test splits for multiple models. Columns Column Type Description problem str Problem statement generated_solutions list Generated solutions with scores success_rate float Fraction of correct generations majority_vote_is_correct int (0/1) Whether majority vote is correct k int Number of samples generated temperature float Sampling temperature max_len int Maximum… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-MATH-generations.tabular10K<n<100K0 likes49 downloads4mo agoHugging Face26LlamaFinetune /OpenMathReasoning-mini-difficultytext10K<n<100K0 likes49 downloads29d agoHugging Face27NovaSky-AI /labeled_numina_difficulty_162Ktext100K<n<1M1 likes46 downloads2y agoHugging Face28argo11 /japanese-math-empirical-difficulty-pilot-50k Japanese Math Empirical Difficulty Pilot 50k This dataset is a 50,000-problem empirical difficulty pilot, not a full empirical labeling of the original 5.66M-row source dataset. It was created for LLM-jp experiment 0399, Team Victory SFT, to validate empirical difficulty label distribution, downstream split behavior, and the rollout/scoring pipeline before attempting labeling at the full 5.6M scale. Current Status This upload uses the v3 scorer with assistant-only… See the full description on the dataset page: https://huggingface.co/datasets/argo11/japanese-math-empirical-difficulty-pilot-50k.tabulartext-generation100K<n<1M0 likes42 downloads3mo agoHugging Face29junlinw /Qwen2.5-7B-Instruct_numina_difficulty_162K_10text10K<n<100K0 likes41 downloads1y agoHugging Face30Slicky325 /dataset_with_difficultyimage1K<n<10K0 likes41 downloads1y agoHugging Face

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