datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nemotron-student-fail-v41-clean-thinking
DeepSeek-V4.1 clean and action-only trajectories with Nemotron outcomes
DeepSeek-V4.1 reward-1 trajectories rebuilt from the complete teacher audit
under v57-test-path-component-boundary+v57-target-source-recheck. The V4.1 reward and trajectory tier do not by themselves prove
that Nemotron failed. Student outcomes are joined from
nemotron-prolike-coverage-audit-20261001.json. A student failure requires either complete
required-test results with reward 0, or an individually… See the full description on the dataset page: https://huggingface.co/datasets/zhiyuanhucs/nemotron-student-fail-v41-clean-thinking.AM-Thinking-v1-Distilled
📘 Dataset Summary
AM-Thinking-v1 and Qwen3-235B-A22B are two reasoning datasets distilled from state-of-the-art teacher models. Each dataset contains high-quality, automatically verified responses generated from a shared set of 1.89 million queries spanning a wide range of reasoning domains.
The datasets share the same format and verification pipeline, allowing for direct comparison and seamless integration into downstream tasks. They are intended to support the development of… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-Thinking-v1-Distilled.Multilingual-Thinking
Dataset summary
Multilingual-Thinking is a reasoning dataset where the chain-of-thought has been translated from English into one of 4 languages: Spanish, French, Italian, and German. The dataset was created by sampling 1k training samples from the SystemChat subset of SmolTalk2 and translating the reasoning traces with another language model.
This dataset was used in the OpenAI Cookbook to fine-tune the OpenAI gpt-oss models.
You can load the dataset using:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking.emolia-thinking
Emolia-Thinking — a VoiceNet-annotated, balanced subset of Emolia
Emolia-Thinking is a richly annotated speech dataset created for the VoiceNet project. It takes a balanced subset of the Emolia corpus — balanced across speaker-embedding clusters and emotion-embedding clusters so that speakers, voices and emotional states are evenly represented rather than dominated by the most common cases — and annotates every clip along the full VoiceNet Extended voice-performance taxonomy… See the full description on the dataset page: https://huggingface.co/datasets/VoiceNet/emolia-thinking.thinking_droid_lerobot_output_qwen3vlthinking-model-activationsllm-jp-4-thinking-sft-data
llm-jp-4-thinking-sft-data
Overview
This dataset is a supervised fine-tuning (SFT) dataset used to train llm-jp-4-*-thinking models.
This dataset is constructed by extracting prompts from multiple data sources and generating reasoning processes and final responses using gpt-oss-120b.
The splits reasoning_low, reasoning_medium, and reasoning_high correspond to different reasoning effort settings used during generation with gpt-oss-120b.
To support the continued development… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/llm-jp-4-thinking-sft-data.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/OpenDataArena/MMFineReason-Full-2.3M-Qwen3-VL-235B-Thinking.llm-jp-4.1-thinking-sft-data
llm-jp-4.1-thinking-sft-data
Overview
This dataset is a supervised fine-tuning (SFT) dataset used to train llm-jp-4.1-*-thinking models.
This dataset is constructed from prompts and conversations collected from multiple data sources. For most subsets, reasoning processes and final responses used for LLM-jp-4.1 SFT were generated or augmented using gpt-oss-120b.
For the tool-calling and agentic data derived from NVIDIA Nemotron datasets, the original conversations… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/llm-jp-4.1-thinking-sft-data.DAPO-Math-17k-Qwen3-235B-A22B-Thinking-2507-rejection-distill
DAPO-Math-17k-Qwen3-235B-A22B-Thinking-2507-rejection-distill
A high-quality Chain-of-Thought (CoT) dataset generated using Qwen/Qwen3-235B-A22B-Thinking-2507 with rejection sampling on BytedTsinghua-SIA/DAPO-Math-17k. This dataset is ideal for SFT distillation training to improve mathematical reasoning capabilities of models.
The dataset format is compatible with LLaMA-Factory for efficient SFT training.
Files
dapo_distill_boxed.json: Single sampling subset (15,129… See the full description on the dataset page: https://huggingface.co/datasets/Yang-Zhou/DAPO-Math-17k-Qwen3-235B-A22B-Thinking-2507-rejection-distill.ouro-1.4b-thinking-evals
Ouro looped-LM experiments
Experiments on ByteDance's Ouro-1.4B-Thinking looped language model, run on a Colab T4 on 2026-09-20:
small GSM8K / MBPP banks, activation-size accounting, linear probes for the loop index, a per-loop logit lens,
and Contrastive Activation Addition steering with a loop sweep.
ouro_eval.ipynb is the full Colab notebook with outputs (includes the transformers 4.54 cache patch Ouro needs).
The recorded residual-stream activations (1.9 GB, probe/after{0,6… See the full description on the dataset page: https://huggingface.co/datasets/mild-rgb/ouro-1.4b-thinking-evals.thinking_furniture_bench_dataset_lerobot_output_qwen3vlthinking_fractal20220817_data_lerobot_output_qwen3vlioi-eval-openrouter_anthropic_claude-3_7-sonnet_thinking-prompt-mem-limitioi-eval-openrouter_google_gemini-2_0-flash-thinking-exp-prompt-mem-limitthinking_bc_z_lerobot_output_qwen3vlnormistral-11b-thinking-trainingMMFineReason-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.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.FineReason-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.epic-thinking
Source
Rows
glaiveai/reasoning-v1-20m
1 999 793
BAAI/OpenSeek-Synthetic-Reasoning-Data-Examples CC
1 267 534
PrimeIntellect/INTELLECT-3-SFT openreasoning_science
1 000 000
PrimeIntellect/INTELLECT-3-SFT am_chat
852 816
nvidia/Nemotron-Cascade-SFT-Stage-1 general
583 612
open-thoughts/OpenThoughts2-1M
541 898
PrimeIntellect/SYNTHETIC-1-SFT-Data
474 810
allenai/Dolci-Think-SFT-7B
334 908
allenai/Dolci-Think-SFT-32B
327 491
GeneralReasoning/GeneralThought-430K
291 946… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/epic-thinking.Qwen3-235B-A22B-Thinking-2507_Qwen3-1.7B_AIME_1983_2024llm-jp-4.1-33b-thinking-dpo-data
llm-jp-4.1-33b-thinking-dpo-data
Overview
This dataset is a Direct Preference Optimization (DPO) dataset used to train llm-jp-4.1-33b-thinking.
It is constructed by pairing multiple candidate responses for a given prompt and selecting preferred (chosen) and non-preferred (rejected) responses. The splits reasoning_low, reasoning_medium, and reasoning_high correspond to different reasoning effort settings used during response generation.
The fields chosen_analysis… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/llm-jp-4.1-33b-thinking-dpo-data.emolia-thinking-balanced-buckets
Emolia-Thinking — Balanced Per-Dimension Bucket Subset
A balanced, per-dimension bucket subset of
VoiceNet/emolia-thinking,
derived from that dataset's zero-shot VoiceNet-dimension labels.
For every VoiceNet voice/prosody/timbre/style dimension, this subset draws a
roughly equal number of clips from each ordinal bucket (0–6), so that
downstream training / probing sees a balanced distribution along each axis
instead of the strongly skewed natural distribution.
How… See the full description on the dataset page: https://huggingface.co/datasets/laion/emolia-thinking-balanced-buckets.explore-thinking-models-internalablation_nemotron_thinking_32k_with_reasoning_effort
Dataset: ablation_nemotron_thinking_32k_with_reasoning_effort
This dataset was uploaded from /mnt/yulan_pretrain/mount/data_final_train/ablation_nemotron_thinking_32k_with_reasoning_effort/stage_1/tmp/.
llm-jp-4.1-32b-a3b-thinking-dpo-data
llm-jp-4.1-32b-a3b-thinking-dpo-data
Overview
This dataset is a Direct Preference Optimization (DPO) dataset used to train llm-jp-4.1-32b-a3b-thinking.
It is constructed by pairing multiple candidate responses for a given prompt and selecting preferred (chosen) and non-preferred (rejected) responses. The splits reasoning_low, reasoning_medium, and reasoning_high correspond to different reasoning effort settings used during response generation.
The fields… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/llm-jp-4.1-32b-a3b-thinking-dpo-data.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.CodeX-2M-Thinking
Modotte
Note: This dataset is part of the lineup CodeX by Modotte. You can get lots of datasets in this same lineup, with the main focus on providing very high-quality datasets for model training and fine-tuning.
This dataset is fully synthetic, curated from high-quality public sources and enhanced with synthetic data generated using both closed and open-source models. It serves as a strong foundation for instruction-based model tuning and fine-tuning, offering one of the… See the full description on the dataset page: https://huggingface.co/datasets/Modotte/CodeX-2M-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.