datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OpenMath-Vision-CoT-10kReason-RFT-CoT-Dataset
🤗 Reason-RFT CoT Dateset
The full dataset used in our project "Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning".
⭐️ Project │ 🌎 Github │ 🔥 Models │ 📑 ArXiv │ 💬 WeChat
🤖 RoboBrain: Aim to Explore ReasonRFT Paradigm to Enhance RoboBrain's Embodied Reasoning Capabilities.
♣️ Quick Start
Please refer to Dataset Preparation
🔥 Overview
Visual reasoning abilities play a crucial role in understanding complex multimodal… See the full description on the dataset page: https://huggingface.co/datasets/tanhuajie2001/Reason-RFT-CoT-Dataset.glaucoma-expert-cot-raw-1077
Glaucoma Expert Chain-of-Thought
Ophthalmologist six-step reasoning reports for fundus photographs, each paired with
a binary glaucoma label. 1,074 cases from LAG and Papila.
Files
file
rows
split
expert_cot_trainval.jsonl
915
train (823) + val (92)
expert_cot_test.jsonl
159
test
images/
1,074
<source>_<id>.jpg
Record schema
{
"id": "1689",
"source": "LAG",
"image": "LAG_1689.jpg",
"split": "train"… See the full description on the dataset page: https://huggingface.co/datasets/yuzhench/glaucoma-expert-cot-raw-1077.LMOD-Cataract-1K-surgical-analysis-cot
Cataract-1K LLM-Generated Surgical Instructions
Dataset Overview
This dataset is derived from the Cataract-1K dataset (part of the LMOD benchmark) and enhanced using Qwen3-VL-30B-A3B-Thinking, a large vision-language model with reasoning capabilities. It is designed for training medical AI systems to provide actionable surgical guidance with transparent reasoning.
Generation Process
Source Data: Cataract-1K processed frames with segmentation annotations… See the full description on the dataset page: https://huggingface.co/datasets/mehti/LMOD-Cataract-1K-surgical-analysis-cot.R-CoTFundus-CoT
Glaucoma Expert Chain-of-Thought
Ophthalmologist six-step reasoning reports for fundus photographs, each paired with
a binary glaucoma label. 1,074 cases from LAG and Papila.
Files
file
rows
glaucoma / not
train.jsonl
823
304 / 519
val.jsonl
92
46 / 46
test.jsonl
159
79 / 80
images/
1,074
<source>_<id>.jpg
Record schema
{
"id": "1689",
"source": "LAG",
"image": "LAG_1689.jpg",
"split": "train",
"final_diagnosis_GT":… See the full description on the dataset page: https://huggingface.co/datasets/harvardairobotics/Fundus-CoT.tvr-sft-va-cot
TVR-SFT-VA-CoT
Visual-Action SFT training data with Chain-of-Thought reasoning for Target Viewpoint Reproduction (TVR), from the paper "Where to Look: Can Foundation Models Reach a Target Viewpoint Through Active Exploration?" [arXiv].
Dataset Description
Same expert trajectories as TVRBench/tvr-sft-va, but each assistant response includes a <think>...</think> reasoning block before the action. The reasoning was generated by GPT-4o and describes the spatial… See the full description on the dataset page: https://huggingface.co/datasets/TVRBench/tvr-sft-va-cot.pusht_96_norm4_visual_nomarker_allstep_thinking_trickiness_cot
PushT norm4 Visual Nomarker All-Step Thinking Trickiness COT
This dataset is derived from successful PushT visual-nomarker trajectories in novastar112/pusht_96_norm4_visual_nomarker.
Each row contains one full successful trajectory from the first move through the final stop action.
Main files:
training/pusht_allstep_thinking_cot.jsonl.gz: 500,000 train rows.
testing/pusht_allstep_thinking_cot.jsonl.gz: 200 test rows.
metadata/final_scan_validation.json: full local scan after repair… See the full description on the dataset page: https://huggingface.co/datasets/novastar112/pusht_96_norm4_visual_nomarker_allstep_thinking_trickiness_cot.ImageGen-CoT-Reward-5K
ImageGen_Reward_Cold_Start
Dataset Summary
This dataset is distilled from GPT-4o for our UnifiedReward-Think-7b cold-start training.
For further details, please refer to the following resources:
📰 Paper: https://arxiv.org/pdf/2505.03318
🪐 Project Page: https://codegoat24.github.io/UnifiedReward/Think
🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
🤗 Dataset Collections:… See the full description on the dataset page: https://huggingface.co/datasets/CodeGoat24/ImageGen-CoT-Reward-5K.CoTIR-Benchpusht_96_int1_visual_nomarker_allstep_thinking_trickiness_cot
PushT int1 Visual Nomarker All-Step Thinking Trickiness COT
This dataset is derived from successful PushT visual-nomarker trajectories in novastar112/pusht_96_int1_visual_nomarker.
Each row contains one full successful trajectory from the first move through the final stop action.
Main files:
training/pusht_allstep_thinking_cot.jsonl.gz: 500,000 train rows.
testing/pusht_allstep_thinking_cot.jsonl.gz: 200 test rows.
Message format:
Each user turn is the PushT prompt text plus one… See the full description on the dataset page: https://huggingface.co/datasets/novastar112/pusht_96_int1_visual_nomarker_allstep_thinking_trickiness_cot.LLaVA-CoT-25k