datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
thinking_droid_lerobot_output_qwen3vlMMFineReason-Full-2.3M-Qwen3-VL-235B-Thinking
MMFineReason-Full-2.3M
The Complete Pre-Selection Dataset — Before Quality Filtering
📖 Overview
MMFineReason-Full-2.3M is the complete pre-selection dataset containing 2.3M samples and 8.8B solution tokens, generated through our reasoning distillation pipeline before the data selection stage. This dataset includes all samples that passed basic template and length validation, but have not undergone correctness verification filtering.
🎯 Key Characteristics… See the full description on the dataset page: https://huggingface.co/datasets/OpenDataArena/MMFineReason-Full-2.3M-Qwen3-VL-235B-Thinking.ImageNet-Think
ImageNet-Think 250K
ImageNet-Think 250K is a large-scale synthetic multimodal reasoning dataset containing of 250,000 images sampled from ImageNet-21K dataset. For each image, we provide a prompt and two different step-by-step reasoning tokens and outputs (answers), enabling evaluation and training for Vision Language Models on reasoning tasks. This dataset is primarily designed for research on multimodal summarization.
Installation & Setup
Before downloading… See the full description on the dataset page: https://huggingface.co/datasets/krishnateja95/ImageNet-Think.thinking_furniture_bench_dataset_lerobot_output_qwen3vlFineReason-1.8M-Qwen3-VL-235B-Thinking
MMFineReason
Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
Average score across mathematical reasoning and multimodal understanding benchmarks.
📖 Overview
MMFineReason is a large-scale, high-quality multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring detailed reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking.
🎯 Key Highlights
1.8M High-Quality Samples with 5.1B Solution Tokens… See the full description on the dataset page: https://huggingface.co/datasets/NarsAI/FineReason-1.8M-Qwen3-VL-235B-Thinking.MMFineReason-1.8M-Qwen3-VL-235B-Thinking
MMFineReason
Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
Average score across mathematical reasoning and multimodal understanding benchmarks.
📖 Overview
MMFineReason is a large-scale, high-quality multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring detailed reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking.
🎯 Key Highlights
1.8M High-Quality Samples with 5.1B Solution Tokens… See the full description on the dataset page: https://huggingface.co/datasets/OpenDataArena/MMFineReason-1.8M-Qwen3-VL-235B-Thinking.MMFineReason-Full-2.3M-Qwen3-VL-235B-Thinking
MMFineReason-Full-2.3M
The Complete Pre-Selection Dataset — Before Quality Filtering
📖 Overview
MMFineReason-Full-2.3M is the complete pre-selection dataset containing 2.3M samples and 8.8B solution tokens, generated through our reasoning distillation pipeline before the data selection stage. This dataset includes all samples that passed basic template and length validation, but have not undergone correctness verification filtering.
🎯 Key Characteristics… See the full description on the dataset page: https://huggingface.co/datasets/ericktwo/MMFineReason-Full-2.3M-Qwen3-VL-235B-Thinking.textlatent_zebra_thinkmorph_armAB
Text-Latent (Arm A) vs All-Latent (Arm B) — Zebra-CoT + ThinkMorph
35638 samples/arm, 18 categories. Schema = ULVR/williamium style (sample_id, category, source_dataset,
question, answer, input_image, intermediate_image_N, num_intermediate_steps, messages_json).
armA_text_latent: real decoded text CoT + latent visual blocks (intermediate_image_1..3).
armB_render_latent: reasoning text RENDERED to images, all-latent baseline (intermediate_image_1..17).
messages_json = full Monet… See the full description on the dataset page: https://huggingface.co/datasets/RuoliuYang/textlatent_zebra_thinkmorph_armAB.MMFineReason-1.8M-Qwen3-VL-235B-Thinking
MMFineReason
Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
Average score across mathematical reasoning and multimodal understanding benchmarks.
📖 Overview
MMFineReason is a large-scale, high-quality multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring detailed reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking.
🎯 Key Highlights
1.8M High-Quality Samples with 5.1B Solution Tokens… See the full description on the dataset page: https://huggingface.co/datasets/Sandeepthakur/MMFineReason-1.8M-Qwen3-VL-235B-Thinking.VLAA-Thinking
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
🌐 Project Page
• 📄 Arxiv
• 💻 Code
🤗 VLAA-Thinker Family
• 🤔 VLAA-Thinking Dataset
🤗 VLAA-Thinker-Qwen2.5-3B
• 🤗 VLAA-Thinker-Qwen2.5-7B
Both VLAA-Thinker-Qwen2.5-3B and VLAA-Thinker-Qwen2.5-7Bachieve SOTA performance on OpenCompass Multimodal Reasoning Leaderboard as of April 7th, 2025.
Contents
Quick Start 🚀… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/VLAA-Thinking.thinking_fmb_dataset_lerobot_output_qwen3vlThinkGeo
ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks
mhlc-training-qwen3vl-qwen3_vl_2b_thinking_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Qwen3-VL 2B Thinking hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-qwen3vl-qwen3_vl_2b_thinking_hard_mixed_sources_120k.spatial-mmcot-thinkmorph_spatial_nav
Spatial MMCoT v1 · thinkmorph_spatial_nav
ThinkMorph (arXiv:2510.27492) Spatial_Navigation: FrozenLake navigation on 3x3 to 6x6 grids. The one input image is the grid, and the question text is the same on every row (upstream's wording, unchanged: it asks for the moves in \boxed{{}}, an unformatted template left in upstream; the read-back boxes the moves while <answer> holds them bare), so the maze exists only in the image. The first thought describes the grid (start, goal… See the full description on the dataset page: https://huggingface.co/datasets/yrlyrl/spatial-mmcot-thinkmorph_spatial_nav.MMFineReason-1.8M-Qwen3-VL-235B-Thinking
MMFineReason
Closing the Multimodal Reasoning Gap via Open Data-Centric Methods
Average score across mathematical reasoning and multimodal understanding benchmarks.
📖 Overview
MMFineReason is a large-scale, high-quality multimodal reasoning dataset comprising 1.8M samples and 5.1B solution tokens, featuring detailed reasoning annotations distilled from Qwen3-VL-235B-A22B-Thinking.
🎯 Key Highlights
1.8M High-Quality Samples with 5.1B Solution Tokens… See the full description on the dataset page: https://huggingface.co/datasets/dans25275/MMFineReason-1.8M-Qwen3-VL-235B-Thinking.spatial-mmcot-thinkmorph_jigsaw
Spatial MMCoT v1 · thinkmorph_jigsaw
ThinkMorph (arXiv:2510.27492) Jigsaw_Assembly: a picture cut into numbered parts, shown in an order that may be shuffled; decide the arrangement, draw the assembled picture, read it back, answer. According to the ThinkMorph paper (arXiv:2510.27492, appendix on data generation), GPT-4.1 wrote the plan and the read-back from the question and the ground-truth answer, and was told not to reveal the answer. The pictures come from 3 corpora: 3,201… See the full description on the dataset page: https://huggingface.co/datasets/yrlyrl/spatial-mmcot-thinkmorph_jigsaw.MMFineReason-SFT-586K-Qwen3-VL-235B-Thinking
MMFineReason-SFT-586K
The Hardest 33% — Less Data, More Reasoning
📖 Overview
MMFineReason-SFT-586K is a difficulty-filtered subset of MMFineReason-1.8M, containing the hardest 33% of samples where Qwen3-VL-4B-Thinking do not consistently succeed. (pass rate ≠ 1).
Specifically, this subset removes all easy samples (pass rate = 1) under Qwen3-VL-4B-Thinking, retaining only instances that require non-trivial multimodal reasoning.
🎯 Key Highlights
586K… See the full description on the dataset page: https://huggingface.co/datasets/OpenDataArena/MMFineReason-SFT-586K-Qwen3-VL-235B-Thinking.finevision-mini-thinking
FineVision-mini Thinking
FineVision-mini is a slice I made of HuggingFaceM4/FineVision:
169 of its image subsets, 101,321 rows (~40 GB) out of FineVision's 24.2M rows / 4.65 TB (about 0.4% of
the rows, 0.9% of the bytes), sampled with a fixed seed. The 16 text-only subsets were left out.
This dataset is that slice, fully translated and augmented with reasoning, published in increments:
each batch processes more rows of FineVision-mini and is appended here, until the whole slice… See the full description on the dataset page: https://huggingface.co/datasets/olob0/finevision-mini-thinking.MMFineReason-SFT-123K-Qwen3-VL-235B-Thinking
MMFineReason-SFT-123K
The Hardest 7% — Less Data, More Reasoning
📖 Overview
MMFineReason-SFT-123K is a difficulty-filtered subset of MMFineReason-1.8M, containing only the hardest 7% of samples where Qwen3-VL-4B-Thinking consistently fails (pass rate = 0).
🎯 Key Highlights
123K Challenging Samples: Only instances where a 4B thinking model fails all 4 inference attemptsEfficient Training: Comparable performance to full 1.8M dataset with only 7% of… See the full description on the dataset page: https://huggingface.co/datasets/OpenDataArena/MMFineReason-SFT-123K-Qwen3-VL-235B-Thinking.Spatial_Navigation
🌟 This repo contains part of the training dataset for model ThinkMorph-7B.
Dataset Description
We create an enriched interleaved dataset centered on four representative tasks requiring varying degrees of visual engagement and cross-modal interactions, including Jigsaw Assembly, Spatial Navigation, Visual Search and Chart Refocus.
Statistics
Dataset Usage
Data Downloading… See the full description on the dataset page: https://huggingface.co/datasets/ThinkMorph/Spatial_Navigation.Jigsaw_Assembly
🌟 This repo contains part of the training dataset for model ThinkMorph-7B.
Dataset Description
We create an enriched interleaved dataset centered on four representative tasks requiring varying degrees of visual engagement and cross-modal interactions, including Jigsaw Assembly, Spatial Navigation, Visual Search and Chart Refocus.
Statistics
Dataset Usage
Data Downloading… See the full description on the dataset page: https://huggingface.co/datasets/ThinkMorph/Jigsaw_Assembly.mhlc-training-qwen3vl-qwen3_vl_4b_thinking_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Qwen3-VL 4B Thinking hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-qwen3vl-qwen3_vl_4b_thinking_hard_mixed_sources_120k.BAGEL-thinkpred_qwen3vl_think_10kshotpath-qwen3vl-thinking-cot-20260714ThinkEditmhlc-training-qwen3.5-qwen3_5_4b_think_off_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Qwen3.5 4B think off hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-qwen3.5-qwen3_5_4b_think_off_hard_mixed_sources_120k.mhlc-training-qwen3.5-qwen3_5_9b_think_off_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Qwen3.5 9B think off hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-qwen3.5-qwen3_5_9b_think_off_hard_mixed_sources_120k.mhlc-training-gemma4-gemma4_e4b_it_think_on_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Gemma 4 E4B it think on hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-gemma4-gemma4_e4b_it_think_on_hard_mixed_sources_120k.Visual_Search
🌟 This repo contains part of the training dataset for model ThinkMorph-7B.
Dataset Description
We create an enriched interleaved dataset centered on four representative tasks requiring varying degrees of visual engagement and cross-modal interactions, including Jigsaw Assembly, Spatial Navigation, Visual Search and Chart Refocus.
Statistics
Dataset Usage
Data Downloading… See the full description on the dataset page: https://huggingface.co/datasets/ThinkMorph/Visual_Search.