datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Multi-Agent_Reinforcement_Learning_Trading_System_Data
📊 Multi-Agent RL Trading System - Dataset
This dataset contains historical OHLCV (Open, High, Low, Close, Volume) data for AAPL, MSFT, and GOOGL, pre-processed for Reinforcement Learning based trading systems.
📁 Dataset Content
The dataset consists of CSV files downloaded via yfinance:
AAPL.csv: Apple Inc. daily data (Jan 2018 - Dec 2024).
MSFT.csv: Microsoft Corp. daily data (Jan 2018 - Dec 2024).
GOOGL.csv: Alphabet Inc. daily data (Jan 2018 - Dec 2024).
📝… See the full description on the dataset page: https://huggingface.co/datasets/AdityaaXD/Multi-Agent_Reinforcement_Learning_Trading_System_Data.Multi-Agent_Reinforcement_Learning_Trading_System_Data
📊 Multi-Agent RL Trading System - Dataset
This dataset contains historical OHLCV (Open, High, Low, Close, Volume) data for AAPL, MSFT, and GOOGL, pre-processed for Reinforcement Learning based trading systems.
📁 Dataset Content
The dataset consists of CSV files downloaded via yfinance:
AAPL.csv: Apple Inc. daily data (Jan 2018 - Dec 2024).
MSFT.csv: Microsoft Corp. daily data (Jan 2018 - Dec 2024).
GOOGL.csv: Alphabet Inc. daily data (Jan 2018 - Dec 2024).… See the full description on the dataset page: https://huggingface.co/datasets/sanjaydoss/Multi-Agent_Reinforcement_Learning_Trading_System_Data.reinforcement-learning-checkpoint-downloadsAgentic-Diagnostic-Reasoning-with-Multimodal-SLMs-via-Reinforcement-Learningreinforcement-learningrepro-exact-unlearning-in-reinforcement-learning-traces
Agent traces
Agent sessions published from a Trackio Logbook.
computer_agent_reinforcement_learning_trajectory_seagent_ai_assistant_tools_agent_mcpreinforcement-learning-results
Reinforcement Learning Results
This repository contains the latest result artifacts for the
prompts-repaired-v1 reinforcement-learning experiments in
ai-pref-drift.
Included scope
Model sizes: Qwen3.5 4B and 9B.
Model states: M0-v4 and DPO, GRPO, and PPO Step 125 (seed 0).
Actual pairwise-preference batteries: coding-task preference and value
preference, each in thinking and non-thinking modes.
Anticipation batteries: coding-task anticipation and value… See the full description on the dataset page: https://huggingface.co/datasets/prism-drift/reinforcement-learning-results.reinforcement_learningNFA_OCR_reinforcement_learning_format_TEST5repro-contextual-rollout-bandits-for-reinforcement-learning-with-verifiable-rewards-artifacts
Reproduction: Contextual Rollout Bandits for RLVR (ICML 2026, #985)
Independent reproduction of "Contextual Rollout Bandits for Reinforcement Learning
with Verifiable Rewards" (Lu, Wang, Chai, Yin, Lin, Chen, Luo, Zhuang, Ban, Wang) —
OpenReview weMYE1B16x,
arXiv 2602.08499.
Part of the Hugging Face × AlphaXiv ICML-2026 reproduction challenge.
Official code: github.com/lxd99/CBS_public (verl 0.5.x fork).
What CBS is
The paper reframes rollout scheduling in RLVR as… See the full description on the dataset page: https://huggingface.co/datasets/debajyotidasgupta/repro-contextual-rollout-bandits-for-reinforcement-learning-with-verifiable-rewards-artifacts.gene-reinforcement-learning-instruct
reinforcement-learning-instruct v4
Gate-passed instruction data for reinforcement-learning — published when 50 fresh examples cleared the quality bar
Kind: synthetic
Domain: reinforcement-learning
Records: 198
Created: 2026-06-19T23:14:20+00:00
SHA-256: 3393dfd6bd9adc38414885ee2f5ac35f6ce60b4c57a98c3e3f2ca78e574f1469
Pipeline: v2.0.0
Filters: {"min_quality": 0.55, "limit": 1000, "source": null, "backend": "llama", "min_judge": 0.7}
Generated by:… See the full description on the dataset page: https://huggingface.co/datasets/Gene829/gene-reinforcement-learning-instruct.motoman-up6-cq-lambda-reinforcement-learning_v1.0
CQ(λ) Bag-Shaking Dataset: Human-in-the-Loop Reinforcement Learning
Dataset Description
This dataset contains synthetic training data comparing standard Q-learning with eligibility traces [Q(λ)] against Cooperative Q-learning [CQ(λ)], a human-in-the-loop reinforcement learning algorithm. The data simulates a robotic "bag-shaking" task where an agent must extract knotted objects from a bag through strategic shaking motions.
Dataset Summary
Task: Bag-shaking… See the full description on the dataset page: https://huggingface.co/datasets/DBbun/motoman-up6-cq-lambda-reinforcement-learning_v1.0.NFA_OCR_reinforcement_learning_format_TEST6Diffusion-Deep-Reinforcement-LearningNFA_OCR_reinforcement_learning_format_TEST4