datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
UltraData-RL-2609
UltraData-RL-2609
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
UltraData-RL-2609 is the L3 refined data for reinforcement learning within UltraData's L0-L4 tiered data management framework. Built for the RL stage of MiniCPM5-2B post-training, it complements UltraData-SFT-2605 with verifiable-reward tasks. It is also the training corpus used by JustRL II (Scaling Small LLMs to 128K Reasoning with a Critic)… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/UltraData-RL-2609.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.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.REDSearcher_RL_1KMath-RL-Tasks
Ulam AI Math RL Tasks
Forty original, verifier-backed mathematical reasoning tasks packaged as ten
independent RL environments. The collection spans advanced graduate exercises,
research-style exact computation and structural generalization problems in
algebraic geometry, arithmetic geometry, combinatorics, topology, probability
and spectral analysis.
Each suite pairs a runnable rl_env/ with a preserved blind_run/ by
GPT-5.6 Sol Pro. The model name describes the evaluation actor… See the full description on the dataset page: https://huggingface.co/datasets/ulamai/Math-RL-Tasks.Multi-subject-RLVRMulti-subject data for paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
we use a multi-subject multiple-choice QA dataset ExamQA (Yu et al., 2021).
Originally written in Chinese, ExamQA covers at least 48 first-level subjects.
We remove the distractors and convert each instance into a free-form QA pair.
This dataset consists of 638k college-level instances, with both questions and objective answers written by domain experts for examination purposes.
We also use GPT-4o-mini… See the full description on the dataset page: https://huggingface.co/datasets/virtuoussy/Multi-subject-RLVR.InSight-doc-RL-19k
InSight-doc-RL-19k
Agentic Visual Perception for Long-Document Understanding
📄 Paper |
💻 Code |
🤗 Model |
🧩 SFT Data |
🎬 Replay Demo |
🚀 Live Demo
Understand the big picture. Focus on the right details. Answer from the evidence.
InSight-doc-RL-19k is the reinforcement-learning corpus used to
train the InSight-doc long-document understanding agent after SFT. It contains
challenging document VQA prompts, unanswerable negatives, multiple-choice… See the full description on the dataset page: https://huggingface.co/datasets/m-Just/InSight-doc-RL-19k.OpenTriviaQAUnofficial port of https://github.com/uberspot/OpenTriviaQA
List of all available dataset splits:
['animals', 'brain-teasers', 'celebrities', 'entertainment', 'for-kids', 'general', 'geography', 'history', 'hobbies', 'humanities', 'literature', 'movies', 'music', 'newest', 'people', 'rated', 'religion-faith', 'science-technology', 'sports', 'television', 'video-games', 'world']
Code to reproduce the dataset:
import os
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq… See the full description on the dataset page: https://huggingface.co/datasets/rlyapin/OpenTriviaQA.Nemotron-RL-ARC-AGI-v1
Dataset Description:
Nemotron-RL-ARC-AGI-v1 is a reinforcement-learning (RL) gym environment dataset of single-step ARC-AGI puzzle prompts intended for RL post-training of large language models. Each row corresponds to one ARC puzzle (a set of (input grid, output grid) demonstration pairs plus a single test input grid) rendered as a text prompt; reward is binary (1.0 / 0.0) determined by exact-match comparison against the ground-truth output grid. No LLM judge is used, no… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-ARC-AGI-v1.RLEP_datasetThis repository contains the datasets used in the paper RLEP: Reinforcement Learning with Experience Replay for LLM Reasoning.
RLEP (Reinforcement Learning with Experience rePlay) is a two-phase framework that first collects verified successful trajectories and then replays them during subsequent training. At every update step, the policy is optimized on mini‑batches that blend newly generated rollouts with these replayed successes. By replaying high‑quality examples, RLEP steers the model… See the full description on the dataset page: https://huggingface.co/datasets/Kwai-Klear/RLEP_dataset.UHR-BAT-SFT-10K
UHR-BAT-SFT-10K
Supervised Fine-Tuning for Ultra-High-Resolution Remote Sensing
Project · Paper · Code
English | 中文
📚 Introduction
UHR-BAT-SFT-10K contains visual question answering style instruction-following examples for ultra-high-resolution remote-sensing imagery. It is the supervised fine-tuning dataset used for UHR-BAT: Budget-Aware Token Compression Vision-Language Model for Ultra-High-Resolution Remote Sensing.… See the full description on the dataset page: https://huggingface.co/datasets/RL-MIND/UHR-BAT-SFT-10K.Nemotron-RL-litmus-bench-v0.1
Dataset Description:
Litmus-Bench v0.1 is an open dataset for training and evaluating chemical reasoning in language models. It includes 5,232 training questions and 482 test questions, each in short-answer format and was created from the ChEMBL dataset with RDKit descriptors requiring short answers. The dataset is for RL training.
This dataset is released as part of NVIDIA NeMo-Gym, an open-source library within the NVIDIA NeMo framework, designed for large-scale, verifiable… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-litmus-bench-v0.1.RL-Collection
RL-Collection-v1: A Unified Verifiable-Rewards Corpus
RL-Collection-v1 is a large-scale, curated corpus for reinforcement
learning from verifiable rewards (RLVR) of reasoning-oriented language
models. It combines, filters, normalises, and deduplicates a broad set of
public RL datasets into a single consistent schema, with each row carrying a
machine-verifiable ground-truth signal (math equivalence, code execution,
Prolog rule induction, schema validation, multiple-choice, …)… See the full description on the dataset page: https://huggingface.co/datasets/ahmad21omar/RL-Collection.Polyglot-Thoughts-RL-Collection
Polyglot Thoughts RL Collection
Polyglot Thoughts RL Collection is a large-scale, curated corpus for
reinforcement learning from verifiable rewards (RLVR) of reasoning-oriented
language models. It combines, filters, normalises, and deduplicates a broad set
of public RL datasets into a single uniform schema in which every row carries a
machine-verifiable ground-truth signal — math equivalence, code execution,
Prolog rule induction, schema validation, multiple-choice… See the full description on the dataset page: https://huggingface.co/datasets/ahmad21omar/Polyglot-Thoughts-RL-Collection.eli5_rlhfELI5 paired
This is a processed version of the eli5 dataset. The dataset was created following very closely the steps in the stack-exchange-paired dataset. The following steps were applied:
Create pairs (response_j, response_k) where j was rated better than k
Sample at most 10 pairs per question
Shuffle the dataset globally
This dataset is designed to be used for preference learning using techniques such as Reinforcement Learning from Human Feedback. The processing notebook is in the… See the full description on the dataset page: https://huggingface.co/datasets/vincentmin/eli5_rlhf.Open-MM-RL
Dataset Summary
Open-MM-RL is a multimodal STEM reasoning dataset covering Physics, Mathematics, Biology, and Chemistry. It is designed for problems that require models to interpret visual information and combine it with step-by-step analytical reasoning.
Explore the full Open-MM-RL dataset (3,000 tasks coming soon): https://go.turing.com/open-mm-rl
Compared with existing multimodal reasoning benchmarks, Open-MM-RL broadens the evaluation setting beyond standard single-image… See the full description on the dataset page: https://huggingface.co/datasets/TuringEnterprises/Open-MM-RL.Nemotron-RL-QA-Abstention-v1Nemotron-RL-QA-Abstention-v1
License: cc-by-4.0
Language: en
Task Categories: reinforcement-learning, question-answering, text-generation
Tags: abstention, question-answering, hotpotqa, software-engineering, health, law, rl, rlvr
Configs: default train split at data/train.jsonl
Domain: multi-domain question answering, abstention
Modality: text
Capability Breakdown: Abstention-aware factoid question answering [100%]
Source: Hybrid: Automated, Manually Collected, Synthetic
Size Bin: <10K… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-QA-Abstention-v1.rlhn-680K
Dataset Card for RLHN-680K
Dataset Description
Repository |
Paper |
ArXiv
RLHN is a cascading LLM framework designed to accurately relabel hard negatives in existing IR/RAG training datasets, such as MS MARCO and HotpotQA.
This Tevatron dataset (680K training pairs) contains the queries, positives + relabeled hard negatives, remaining hard negatives for 7 datasets in the BGE training collection.
This repository contains the training pairs that can be used to fine-tune… See the full description on the dataset page: https://huggingface.co/datasets/rlhn/rlhn-680K.RLPR-Evaluation
Dataset Card for RLPR-Evaluation
GitHub | Paper
News:
[2025.06.23] 📃 Our paper detailing the RLPR framework and its comprehensive evaluation using this suite is accessible at here!
Dataset Summary
We include the following seven benchmarks for evaluation of RLPR:
Mathematical Reasoning Benchmarks:
MATH-500 (Cobbe et al., 2021)
Minerva (Lewkowycz et al., 2022)
AIME24
General Domain Reasoning Benchmarks:
MMLU-Pro (Wang et al., 2024): A multitask language… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/RLPR-Evaluation.rLLM-FinQA-Dataset
Data
Our dataset consists of financial Q&A pairs derived from SEC 10-K filings across 207 public companies:
5,110 single-table questions.
1,248 multi-table questions.
6,923 financial tables.
Format
Each row in the dataset contains:
user_query: The question with company context.
question: The core question.
answer: The ground truth answer.
explanation: The step-by-step solution.
company: The company name.
table_name: The table(s) needed to answer.
question_type: The… See the full description on the dataset page: https://huggingface.co/datasets/rLLM/rLLM-FinQA-Dataset.Open-RL
Open-RL
Dataset Summary
This dataset contains self-contained, verifiable, and unambiguous STEM reasoning problems across Physics, Mathematics, Biology, and Chemistry.
Each problem:
Requires multi-step reasoning
Involves symbolic manipulation and/or numerical computation
Has a deterministic, objectively verifiable final answer
The problems were evaluated against contemporary large language models. Observed pass rates indicate that the tasks are non-trivial yet… See the full description on the dataset page: https://huggingface.co/datasets/TuringEnterprises/Open-RL.UltraData-RL-2609
UltraData-RL-2609
📦 UltraData Collection |
🌐 UltraData |
🤗 MiniCPM5 Series
English |
中文
📚 Introduction
UltraData-RL-2609 is the L3 refined data for reinforcement learning within UltraData's L0-L4 tiered data management framework. Built for the RL stage of MiniCPM5-2B post-training, it complements UltraData-SFT-2605 with verifiable-reward tasks. It is also the training corpus used by JustRL II (Scaling Small LLMs to 128K Reasoning with a Critic)… See the full description on the dataset page: https://huggingface.co/datasets/kwakuobeng/UltraData-RL-2609.ODA-Fin-RL-12k
Unlocking Data Value in Finance: A Study on Distillation
and Difficulty-Aware Training
📖 Overview
ODA-Fin-RL-12K is a carefully curated dataset for reinforcement learning (RL) in financial domain, comprising 12,187 hard-but-verifiable samples. Designed to complement ODA-Fin-SFT-318K, this dataset targets challenging financial reasoning tasks with concise, reliably verifiable answers—optimized for RL training.
🎯 Key Highlights
12K Hard Samples: Curated… See the full description on the dataset page: https://huggingface.co/datasets/OpenDataArena/ODA-Fin-RL-12k.eli5_rlhf_explainlikeim5
ELI5 paired
This is a processed version of the eli5 dataset.
Compared to "eli5_rlhf", this dataset contains only QA pairs from the train split of the eli5 dataset and only from the subreddit explainlikeimfive.
Furthermore, the function
def get_question(example):
title = example["title"]
selftext = example["selftext"]
if selftext:
if selftext[-1] not in [".", "?", "!"]:
seperator = ". "
else:
seperator = " "
question = title… See the full description on the dataset page: https://huggingface.co/datasets/vincentmin/eli5_rlhf_explainlikeim5.MMR1-RL
MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources
Introduction
This repository introduces the MMR1 project, focusing on enhancing large multimodal reasoning models. While rapid progress has been made, advancements are constrained by two major limitations:
The absence of open, large-scale, high-quality long chain-of-thought (CoT) data.
The instability of reinforcement learning (RL) algorithms in post-training, where standard Group… See the full description on the dataset page: https://huggingface.co/datasets/MMR1/MMR1-RL.chem-rlvr-TEST-4
ChemBench-RLVR: Comprehensive Chemistry Dataset for Reinforcement Learning from Verifiable Rewards
Dataset Description
ChemBench-RLVR is a high-quality, balanced dataset containing 16,699 question-answer pairs across 14 chemistry task types. This dataset is specifically designed for training language models using Reinforcement Learning from Verifiable Rewards (RLVR), where all answers are computationally verifiable using established cheminformatics tools.
Key… See the full description on the dataset page: https://huggingface.co/datasets/summykai/chem-rlvr-TEST-4.RLHF-VBorrowed from: https://huggingface.co/datasets/openbmb/RLHF-V-Dataset
You can use it in LLaMA Factory by specifying dataset: rlhf_v.
chem-rlvr-TEST
ChemBench-RLVR: Comprehensive Chemistry Dataset for Reinforcement Learning from Verifiable Rewards
Dataset Description
ChemBench-RLVR is a high-quality, balanced dataset containing 7,001 question-answer pairs across 14 chemistry task types. This dataset is specifically designed for training language models using Reinforcement Learning from Verifiable Rewards (RLVR), where all answers are computationally verifiable using established cheminformatics tools.
Key… See the full description on the dataset page: https://huggingface.co/datasets/summykai/chem-rlvr-TEST.