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A Survey of Reinforcement Learning for Large Reasoning Models
A Framework for LLM-based Multi-Agent Reinforced Training and Inference
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
Scalable RL solution for advanced reasoning of language models
Benchmarking Knowledge Transfer in Lifelong Robot Learning
Multi-Joint dynamics with Contact. A general purpose physics simulator.
GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment
The paper list of the 86-page SCIS cover paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.
Code and implementations for the ACL 2025 paper "AgentGym: Evolving Large Language Model-based Agents across Diverse Environments" by Zhiheng Xi et al.
A library for advanced large language model reasoning
COPRA: An in-COntext PRoof Agent which uses LLMs like GPTs to prove theorems in formal languages.
Awesome-LLM: a curated list of Large Language Model
GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22
A fundational graph learning framework that solves cross-domain/cross-task classification problems using one model.
A guidance language for controlling large language models.
From Chain-of-Thought prompting to OpenAI o1 and DeepSeek-R1 🍓
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
Benchmark datasets, data loaders, and evaluators for graph machine learning
[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner in WWW'23
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Robotics Toolbox for Python
"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022
The GitHub repository for the paper: “Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction“. (NeurIPS 2022)