datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Spreadsheet-RL
Spreadsheet-RL Dataset
Project Page | Paper | GitHub | Model
This dataset contains the training and evaluation data used by Spreadsheet-RL, a reinforcement learning framework for spreadsheet agents that edit Excel workbooks with tools and receive outcome-based rewards from workbook recalculation and answer-range comparison.
News
🚀 2026-08-01: Released the Spreadsheet-RL-8B checkpoint, scaling SpreadsheetBench Pass@1 from 15.9% for the base model to 16.7%… See the full description on the dataset page: https://huggingface.co/datasets/Spreadsheet-RL/Spreadsheet-RL.NuminaMath-1.5-RL-Verifiable
Dataset Card for NuminaMath-1.5-RL-Verifiable
Dataset Summary
NuminaMath-1.5-RL-Verifiable is a curated subset of the NuminaMath-1.5 dataset, specifically filtered to support reinforcement learning applications requiring verifiable outcomes. This collection consists of 131,063 math word problems from the original dataset that meet strict filtering criteria: all problems have definitive numerical answers, validated problem statements and solutions, and come from… See the full description on the dataset page: https://huggingface.co/datasets/nlile/NuminaMath-1.5-RL-Verifiable.hle-gpt-oss-120b-no-python-260222
hle-gpt-oss-120b-no-python-260222
Deep research agent evaluation on rl-rag/hle_text_only (test split).
Results
Metric
Value
pass@4
47.9%
avg@4
26.6%
Trajectory accuracy
26.6% (2292/8632)
Questions
2158
Trajectories
8632 (4 per question)
Avg tool calls
14.5
Full conversations
❌
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
50
Temperature
0.7
Blocked domains
huggingface.co
Tool Usage… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/hle-gpt-oss-120b-no-python-260222.XHRBench
XHRBench
Ultra-High-Resolution Remote Sensing Understanding and Reasoning
🤗 Hugging Face ·
🤖 ModelScope ·
📄 Paper ·
💻 Code
English | 中文
📚 Introduction
XHRBench evaluates fine-grained perception and complex reasoning in multimodal large language models using ultra-high-resolution remote-sensing imagery. This repository retains the name XHRBench and belongs to the same RSHR benchmark project as RSHR-Bench, with a… See the full description on the dataset page: https://huggingface.co/datasets/RL-MIND/XHRBench.Multi-SWE-RL-Verified
Multi-SWE-RL-Verified
Gold-patch-validated subset of
PrimeIntellect/Multi-SWE-RL-Reupload
(ByteDance's Multi-SWE-RL): 2,232 / 4,703 rows across
C, Go, Java, JavaScript, Rust, and TypeScript that produce a clean reward signal end-to-end.
Default dataset of the multiswe_v1 taskset.
Changes vs upstream
Starting from the 4,703-row re-upload:
C++ dropped wholesale — 0/449 rows passed gold-patch validation in pass 1; the images are
broken for scoring, not merely… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/Multi-SWE-RL-Verified.Reverse-Text-RL
Reverse-Text-RL
A small, scrappy RL dataset used in prime-rl's CI to debug RL training asking a model to reverse small sentences character-by-character. Follows the general format of PrimeIntellect/Reverse-Text-SFT
The following script was used to generate the dataset.
from datasets import Dataset, load_dataset
dataset = load_dataset("willcb/R1-reverse-wikipedia-paragraphs-v1-1000", split="train")
prompt = "Reverse the text character-by-character. Put your answer in… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/Reverse-Text-RL.Big-Math-RL-Verified
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Big-Math is the largest open-source dataset of high-quality mathematical problems, curated specifically for reinforcement learning (RL) training in language models. With over 250,000 rigorously filtered and verified problems, Big-Math bridges the gap between quality and quantity, establishing a robust foundation for advancing reasoning in LLMs.
Request Early Access to Private… See the full description on the dataset page: https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified.KodCode-Light-RL-10K
🐱 KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding
KodCode is the largest fully-synthetic open-source dataset providing verifiable solutions and tests for coding tasks. It contains 12 distinct subsets spanning various domains (from algorithmic to package-specific knowledge) and difficulty levels (from basic coding exercises to interview and competitive programming challenges). KodCode is designed for both supervised fine-tuning (SFT) and RL tuning.
🕸️… See the full description on the dataset page: https://huggingface.co/datasets/KodCode/KodCode-Light-RL-10K.ZwZ-RL-VQA
ZwZ-RL-VQA: Region-to-Image Distilled Training Data for Fine-Grained Perception
This synthetic dataset is generated via Region-to-Image Distillation (R2I) for training multimodal large language models (MLLMs) on fine-grained perception tasks without test-time tool use.
📖 Overview
The Zooming without Zooming (ZwZ) method transforms "zooming" from an inference-time tool into a training-time primitive:
Zoom-in Synthesis: Strong teacher models (Qwen3-VL-235B, GLM-4.5V)… See the full description on the dataset page: https://huggingface.co/datasets/inclusionAI/ZwZ-RL-VQA.INTELLECT-3-RLREDSearcher_RL_1KRL-Claude-Creative-Writing-SFT
RL-Claude-Creative-Writing-SFT
Alpaca-format dataset. Columns: instruction, input, output
from datasets import load_dataset
ds = load_dataset("SLoonker/RL-Claude-Creative-Writing-SFT", split="train")
RLVR-IFeval
IF Data - RLVR Formatted
This dataset contains instruction following data formatted for use with open-instruct - specifically reinforcement learning with verifiable rewards.
Prompts with verifiable constraints generated by sampling from the Tulu 2 SFT mixture and randomly adding constraints from IFEval.
Part of the Tulu 3 release, for which you can see models here and datasets here.
Dataset Structure
Each example in the dataset contains the standard instruction-tuning… See the full description on the dataset page: https://huggingface.co/datasets/allenai/RLVR-IFeval.guru-RL-92k
Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
Dataset Description
Guru is a curated six-domain dataset for training large language models (LLM) for complex reasoning with reinforcement learning (RL). The dataset contains 91.9K high-quality samples spanning six diverse reasoning-intensive domains, processed through a comprehensive five-stage curation pipeline to ensure both domain diversity and reward verifiability.… See the full description on the dataset page: https://huggingface.co/datasets/IFM/guru-RL-92k.RLAIF-V-Dataset
Dataset Card for RLAIF-V-Dataset
This dataset was introduced in RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness.
GitHub
This dataset was also used in MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
News:
[2025.09.18] 🎉 Our data is used in the powerful MiniCPM-V 4.5 model, which represents a state-of-the-art end-side MLLM achieving GPT-4o level performance!
[2025.03.01] 🎉 RLAIF-V is accepted by CVPR… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset.search-evalbrowsecomp-gpt-oss-120b-260222
browsecomp-gpt-oss-120b-260222
Deep research agent evaluation on data/browsecomp.jsonl (normal split).
Results
Metric
Value
pass@4
46.8%
avg@4
23.9%
Trajectory accuracy
23.9% (1211/5064)
Questions
1266
Trajectories
5064 (4 per question)
Avg tool calls
26.1
Full conversations
✅
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
50
Temperature
0.7
Blocked domains
huggingface.co
Tool Usage… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/browsecomp-gpt-oss-120b-260222.Nemotron-RL-knowledge-mcqa
Dataset Description:
The Nemotron-RL-knowledge-mcqa is a multi-domain synthetic multiple-choice question-answering (MCQA) dataset containing knowledge based questions. It combines and refines subsets of the [OpenScienceReasoning-2] (https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) dataset and other unstructured sources such as books and articles.The dataset was created using Qwen3-32B, [Qwen3-235B-A22B-Instruct-2507]… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-knowledge-mcqa.Multi-SWE-RL-Reupload
Multi-SWE-RL-Reupload
Verbatim re-upload of ByteDance's community-sourced
Multi-SWE-RL
(paper): 4,703 containerized issue-resolving tasks across
C, C++, Go, Java, JavaScript, Rust, and TypeScript.
For training, prefer
PrimeIntellect/Multi-SWE-RL-Verified,
the gold-patch-validated subset of this data.
Changes vs upstream
Storage schema only: per-test maps are stored as columnar struct-of-lists so the rows load
cleanly with datasets (the upstream nested structs… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/Multi-SWE-RL-Reupload.dsqabrowsecomp-no-scroll-gpt-oss-120b
browsecomp-no-scroll-gpt-oss-120b
Deep research agent evaluation on data/browsecomp.jsonl (normal split).
Results
Metric
Value
pass@4
46.0%
avg@4
22.9%
Trajectory accuracy
22.9% (1160/5064)
Questions
1266
Trajectories
5064 (4 per question)
Avg tool calls
27.0
Full conversations
❌
Model & Setup
Model
gpt-oss-120b
Judge
gpt-4o
Max tool calls
50
Temperature
0.7
Blocked domains
huggingface.co
Tool… See the full description on the dataset page: https://huggingface.co/datasets/rl-rag/browsecomp-no-scroll-gpt-oss-120b.rl-lm-imdb-promptsLiteCoder-Terminal-RL-preview
LiteCoder-Terminal-RL-preview
Paper | Code | Blog Post
This dataset contains 602 standardized Harbor terminal environments and was released as part of the paper LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents.
Unlike static text-only instructions, these environments are fully executable and are designed to support the training of terminal-based agents.
Environment Generation Pipeline
The lack of high-quality, executable… See the full description on the dataset page: https://huggingface.co/datasets/Lite-Coder/LiteCoder-Terminal-RL-preview.guru-RL-92k-extra-info-compressed
Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
Note for this extra-info-compressed data version!
The dataset provided in this repository is specifically intended for use with the latest release of VeRL (v0.4.0). Since VeRL rl_dataset.py processes datasets as datasets.Dataset, it is essential that the structure of all Parquet files remains fully consistent. This repository is designed to meet that requirement.
In this repo, the… See the full description on the dataset page: https://huggingface.co/datasets/IFM/guru-RL-92k-extra-info-compressed.Agentic-Multi-SWE-RLresults_v2
Dataset Card for "results_v2"
Leaderboard
More Information needed
alignment-faking-rl
Transcripts from Towards training-time mitigations for alignment faking in RL
This dataset contains the full evaluation transcripts through the RL runs for all model organisms in our blog post, Towards training-time mitigations for alignment faking in RL.
Each file in encrypted_transcripts/ corresponds to one RL training run.
Precautions against pretraining data poisoning
In order to avoid our model organisms' misaligned reasoning from accidentally appearing in… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/alignment-faking-rl.Nemotron-RL-coding-competitive_coding
Dataset Description:
The Nemotron-RL-coding-competitive_coding dataset is a python-only, reasoning-based, synthetic dataset. It contains competitive coding style problems and their unit test cases. These questions and test cases are collected from CodeContests (deepmind/code_contests), and Open-R1 (open-r1/codeforces)
.
This dataset is released as part of NVIDIA NeMo Gym, a framework for building reinforcement learning environments to train large language models. NeMo Gym contains a… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-coding-competitive_coding.VBVR-Pro-RL
VBVR-Pro-RL
The reinforcement-learning split of VBVR-Pro: 50 parameterized tasks × 1,000 instances, held out from the SFT splits, in both a video (TI2V) and an interleaved-image setting.
At a glance
Property
Value
Tasks
50
Instances per… See the full description on the dataset page: https://huggingface.co/datasets/Video-Reason/VBVR-Pro-RL.deep_research_bench_eval