Comparing Observation and Action Representations for Deep Reinforcement Learning in MicroRTS. - Shengyi Huang; Santiago Ontañón (CoRR, 2019)

Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games. - Shengyi Huang; Santiago Ontañón (CoRR, 2020)

Griddly: A platform for AI research in games. - Chris Bamford 001; Shengyi Huang; Simon M. Lucas (CoRR, 2020)

Gym-µRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning. - Shengyi Huang; Santiago Ontañón; Chris Bamford 001; Lukasz Grela (CoG, 2021)

An Empirical Investigation of Early Stopping Optimizations in Proximal Policy Optimization. - Rousslan Fernand Julien Dossa; Shengyi Huang; Santiago Ontañón; Takashi Matsubara 001 (IEEE Access, 2021)

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms. - Shengyi Huang; Rousslan Fernand Julien Dossa; Chang Ye; Jeff Braga (CoRR, 2021)

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms. - Shengyi Huang; Santiago Ontañón (FLAIRS, 2022)

EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine. - Jiayi Weng; Min Lin; Shengyi Huang; Bo Liu 039; Denys Makoviichuk; Viktor Makoviychuk; Zichen Liu; Yufan Song; Ting Luo; Yukun Jiang; Zhongwen Xu; Shuicheng Yan (NeurIPS, 2022)

A2C is a special case of PPO. - Shengyi Huang; Anssi Kanervisto; Antonin Raffin; Weixun Wang; Santiago Ontañón; Rousslan Fernand Julien Dossa (CoRR, 2022)

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms. - Shengyi Huang; Rousslan Fernand Julien Dossa; Chang Ye; Jeff Braga; Dipam Chakraborty; Kinal Mehta; João G. M. Araújo (J. Mach. Learn. Res., 2022)

Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks. - Ryan Sullivan; Akarsh Kumar; Shengyi Huang; John P. Dickerson; Joseph Suarez (NeurIPS, 2023)

Zephyr: Direct Distillation of LM Alignment. - Lewis Tunstall; Edward Beeching; Nathan Lambert 001; Nazneen Rajani; Kashif Rasul; Younes Belkada; Shengyi Huang; Leandro von Werra; Clémentine Fourrier; Nathan Habib; Nathan Sarrazin; Omar Sanseviero; et al. (CoRR, 2023)

Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform. - Shengyi Huang; Jiayi Weng; Rujikorn Charakorn; Min Lin; Zhongwen Xu; Santiago Ontañón (ICLR, 2024)

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning. - Shengyi Huang; Quentin Gallouédec; Florian Felten; Antonin Raffin; Rousslan Fernand Julien Dossa; Yanxiao Zhao; Ryan Sullivan; Viktor Makoviychuk; Denys Makoviichuk; Mohamad H. Danesh; Cyril Roumégous; Jiayi Weng; et al. (CoRR, 2024)

The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization. - Shengyi Huang; Michael Noukhovitch; Arian Hosseini; Kashif Rasul; Weixun Wang; Lewis Tunstall (CoRR, 2024)

TÜLU 3: Pushing Frontiers in Open Language Model Post-Training. - Nathan Lambert 001; Jacob Morrison; Valentina Pyatkin; Shengyi Huang; Hamish Ivison; Faeze Brahman; Lester James V. Miranda; Alisa Liu; Nouha Dziri; Shane Lyu; Yuling Gu; Saumya Malik; et al. (CoRR, 2024)

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models. - Michael Noukhovitch; Shengyi Huang; Sophie Xhonneux; Arian Hosseini; Rishabh Agarwal; Aaron C. Courville (ICLR, 2025)

2 OLMo 2 Furious. - Team OLMo; Pete Walsh; Luca Soldaini; Dirk Groeneveld; Kyle Lo; Shane Arora; Akshita Bhagia; Yuling Gu; Shengyi Huang; Matt Jordan; Nathan Lambert 001; Dustin Schwenk; et al. (CoRR, 2025)

Generalizing Verifiable Instruction Following. - Valentina Pyatkin; Saumya Malik; Victoria Graf; Hamish Ivison; Shengyi Huang; Pradeep Dasigi; Nathan Lambert 001; Hannaneh Hajishirzi (CoRR, 2025)

Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning. - Zihe Liu; Jiashun Liu; Yancheng He; Weixun Wang; Jiaheng Liu; Ling Pan; Xinyu Hu; Shaopan Xiong; Ju Huang; Jian Hu; Shengyi Huang; Siran Yang; et al. (CoRR, 2025)