Variational Deep Q Network. - Yunhao Tang; Alp Kucukelbir (CoRR, 2017)

Exploration by Distributional Reinforcement Learning. - Yunhao Tang; Shipra Agrawal 001 (IJCAI, 2018)

Implicit Policy for Reinforcement Learning. - Yunhao Tang; Shipra Agrawal 001 (CoRR, 2018)

Boosting Trust Region Policy Optimization by Normalizing Flows Policy. - Yunhao Tang; Shipra Agrawal 001 (CoRR, 2018)

KAMA-NNs: Low-dimensional Rotation Based Neural Networks. - Krzysztof Choromanski; Aldo Pacchiano; Jeffrey Pennington; Yunhao Tang (AISTATS, 2019)

Orthogonal Estimation of Wasserstein Distances. - Mark Rowland 001; Jiri Hron; Yunhao Tang; Krzysztof Choromanski; Tamás Sarlós; Adrian Weller (AISTATS, 2019)

Provably Robust Blackbox Optimization for Reinforcement Learning. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Deepali Jain; Yuxiang Yang 007; Atil Iscen; Jasmine Hsu; Vikas Sindhwani (CoRL, 2019)

From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Vikas Sindhwani (NeurIPS, 2019)

Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang (CoRR, 2019)

Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy. - Yunhao Tang; Mingzhang Yin; Mingyuan Zhou (CoRR, 2019)

Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang (CoRR, 2019)

Wasserstein Reinforcement Learning. - Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Anna Choromanska; Krzysztof Choromanski; Michael I. Jordan (CoRR, 2019)

Reinforcement Learning with Chromatic Networks. - Xingyou Song; Krzysztof Choromanski; Jack Parker-Holder; Yunhao Tang; Wenbo Gao; Aldo Pacchiano; Tamás Sarlós; Deepali Jain; Yuxiang Yang 007 (CoRR, 2019)

Discretizing Continuous Action Space for On-Policy Optimization. - Yunhao Tang; Shipra Agrawal 001 (AAAI, 2020)

Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang (AISTATS, 2020)

Variance Reduction for Evolution Strategies via Structured Control Variates. - Yunhao Tang; Krzysztof Choromanski; Alp Kucukelbir (AISTATS, 2020)

Discrete Action On-Policy Learning with Action-Value Critic. - Yuguang Yue; Yunhao Tang; Mingzhang Yin; Mingyuan Zhou (AISTATS, 2020)

ES-MAML: Simple Hessian-Free Meta Learning. - Xingyou Song; Wenbo Gao; Yuxiang Yang 007; Krzysztof Choromanski; Aldo Pacchiano; Yunhao Tang (ICLR, 2020)

Monte-Carlo Tree Search as Regularized Policy Optimization. - Jean-Bastien Grill; Florent Altché; Yunhao Tang; Thomas Hubert; Michal Valko; Ioannis Antonoglou; Rémi Munos (ICML, 2020)

Learning to Score Behaviors for Guided Policy Optimization. - Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Krzysztof Choromanski; Anna Choromanska; Michael I. Jordan (ICML, 2020)

Reinforcement Learning for Integer Programming: Learning to Cut. - Yunhao Tang; Shipra Agrawal 001; Yuri Faenza (ICML, 2020)

Taylor Expansion Policy Optimization. - Yunhao Tang; Michal Valko; Rémi Munos (ICML, 2020)

Self-Imitation Learning via Generalized Lower Bound Q-learning. - Yunhao Tang (NeurIPS, 2020)

Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies. - Yunhao Tang; Krzysztof Choromanski (CoRR, 2020)

Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning. - Yunhao Tang; Alp Kucukelbir (AISTATS, 2021)

Guiding Evolutionary Strategies with Off-Policy Actor-Critic. - Yunhao Tang (AAMAS, 2021)

Revisiting Peng's Q(λ) for Modern Reinforcement Learning. - Tadashi Kozuno; Yunhao Tang; Mark Rowland 001; Rémi Munos; Steven Kapturowski; Will Dabney; Michal Valko; David Abel (ICML, 2021)

Taylor Expansion of Discount Factors. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Michal Valko (ICML, 2021)

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Rémi Munos; Michal Valko (NeurIPS, 2021)

ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning. - Xingyou Song; Krzysztof Choromanski; Jack Parker-Holder; Yunhao Tang; Daiyi Peng; Deepali Jain; Wenbo Gao; Aldo Pacchiano; Tamás Sarlós; Yuxiang Yang 007 (CoRR, 2021)

Unlocking Pixels for Reinforcement Learning via Implicit Attention. - Krzysztof Choromanski; Deepali Jain; Jack Parker-Holder; Xingyou Song; Valerii Likhosherstov; Anirban Santara; Aldo Pacchiano; Yunhao Tang; Adrian Weller (CoRR, 2021)

Reinforcement Learning: New Algorithms and An Application for Integer Programming. - Yunhao Tang (2021)

Marginalized Operators for Off-policy Reinforcement Learning. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Michal Valko (AISTATS, 2022)

Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning. - Yunhao Tang (ICML, 2022)

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. - Daniil Tiapkin; Denis Belomestny; Eric Moulines; Alexey Naumov; Sergey Samsonov; Yunhao Tang; Michal Valko; Pierre Ménard (ICML, 2022)

BYOL-Explore: Exploration by Bootstrapped Prediction. - Zhaohan Guo; Shantanu Thakoor; Miruna Pislar; Bernardo Ávila Pires; Florent Altché; Corentin Tallec; Alaa Saade; Daniele Calandriello; Jean-Bastien Grill; Yunhao Tang; Michal Valko; Rémi Munos; et al. (NeurIPS, 2022)

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Bernardo Ávila Pires; Will Dabney; Marc G. Bellemare (NeurIPS, 2022)

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal. - Tadashi Kozuno; Wenhao Yang; Nino Vieillard; Toshinori Kitamura; Yunhao Tang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Michal Valko; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (CoRR, 2022)

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. - Yash Chandak; Shantanu Thakoor; Zhaohan Daniel Guo; Yunhao Tang; Rémi Munos; Will Dabney; Diana L. Borsa (ICML, 2023)

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice. - Toshinori Kitamura; Tadashi Kozuno; Yunhao Tang; Nino Vieillard; Michal Valko; Wenhao Yang; Jincheng Mei; Pierre Ménard; Mohammad Gheshlaghi Azar; Rémi Munos; Olivier Pietquin; Matthieu Geist; et al. (ICML, 2023)

Quantile Credit Assignment. - Thomas Mesnard; Wenqi Chen; Alaa Saade; Yunhao Tang; Mark Rowland 001; Theophane Weber; Clare Lyle; Audrunas Gruslys; Michal Valko; Will Dabney; Georg Ostrovski; Eric Moulines; et al. (ICML, 2023)

The Edge of Orthogonality: A Simple View of What Makes BYOL Tick. - Pierre Harvey Richemond; Allison C. Tam; Yunhao Tang; Florian Strub; Bilal Piot; Felix Hill (ICML, 2023)

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. - Mark Rowland 001; Yunhao Tang; Clare Lyle; Rémi Munos; Marc G. Bellemare; Will Dabney (ICML, 2023)

Understanding Self-Predictive Learning for Reinforcement Learning. - Yunhao Tang; Zhaohan Daniel Guo; Pierre Harvey Richemond; Bernardo Ávila Pires; Yash Chandak; Rémi Munos; Mark Rowland 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 2023)

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm. - Yunhao Tang; Tadashi Kozuno; Mark Rowland 001; Anna Harutyunyan; Rémi Munos; Bernardo Ávila Pires; Michal Valko (ICML, 2023)

Towards a better understanding of representation dynamics under TD-learning. - Yunhao Tang; Rémi Munos (ICML, 2023)

VA-learning as a more efficient alternative to Q-learning. - Yunhao Tang; Rémi Munos; Mark Rowland 001; Michal Valko (ICML, 2023)

Fast Rates for Maximum Entropy Exploration. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Rémi Munos; Alexey Naumov; Pierre Perrault; Yunhao Tang; Michal Valko; Pierre Ménard (ICML, 2023)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Zhaohan Daniel Guo; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Andrea Michi; Marco Selvi; Sertan Girgin; et al. (CoRR, 2023)

Learning Uncertainty-Aware Temporally-Extended Actions. - Joongkyu Lee; Seung Joon Park; Yunhao Tang; Min-hwan Oh (AAAI, 2024)

Human Alignment of Large Language Models through Online Preference Optimisation. - Daniele Calandriello; Zhaohan Daniel Guo; Rémi Munos; Mark Rowland 001; Yunhao Tang; Bernardo Ávila Pires; Pierre Harvey Richemond; Charline Le Lan; Michal Valko; Tianqi Liu 002; Rishabh Joshi; Zeyu Zheng; et al. (ICML, 2024)

Nash Learning from Human Feedback. - Rémi Munos; Michal Valko; Daniele Calandriello; Mohammad Gheshlaghi Azar; Mark Rowland 001; Daniel Guo 001; Yunhao Tang; Matthieu Geist; Thomas Mesnard; Côme Fiegel; Andrea Michi; Marco Selvi; et al. (ICML, 2024)

Generalized Preference Optimization: A Unified Approach to Offline Alignment. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Rémi Munos; Mark Rowland 001; Pierre Harvey Richemond; Michal Valko; Bernardo Ávila Pires; Bilal Piot (ICML, 2024)

A Distributional Analogue to the Successor Representation. - Harley Wiltzer; Jesse Farebrother; Arthur Gretton; Yunhao Tang; André Barreto 001; Will Dabney; Marc G. Bellemare; Mark Rowland 001 (ICML, 2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Kevin Kevin Li; Rémi Munos; Clare Lyle; Yunhao Tang; Will Dabney (NeurIPS, 2024)

On scalable oversight with weak LLMs judging strong LLMs. - Zachary Kenton; Noah Y. Siegel; János Kramár; Jonah Brown-Cohen; Samuel Albanie; Jannis Bulian; Rishabh Agarwal; David Lindner; Yunhao Tang; Noah D. Goodman; Rohin Shah (NeurIPS, 2024)

Off-policy Distributional Q(λ): Distributional RL without Importance Sampling. - Yunhao Tang; Mark Rowland 001; Rémi Munos; Bernardo Ávila Pires; Will Dabney (CoRR, 2024)

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model. - Mark Rowland 001; Li Kevin Wenliang; Rémi Munos; Clare Lyle; Yunhao Tang; Will Dabney (CoRR, 2024)

Understanding the performance gap between online and offline alignment algorithms. - Yunhao Tang; Zhaohan Daniel Guo; Zeyu Zheng; Daniele Calandriello; Yuan Cao; Eugene Tarassov; Rémi Munos; Bernardo Ávila Pires; Michal Valko; Yong Cheng; Will Dabney (CoRR, 2024)

Offline Regularised Reinforcement Learning for Large Language Models Alignment. - Pierre Harvey Richemond; Yunhao Tang; Daniel Guo 001; Daniele Calandriello; Mohammad Gheshlaghi Azar; Rafael Rafailov; Bernardo Ávila Pires; Eugene Tarassov; Lucas Spangher; Will Ellsworth; Aliaksei Severyn; Jonathan Mallinson; et al. (CoRR, 2024)

An Analysis of Quantile Temporal-Difference Learning. - Mark Rowland 001; Rémi Munos; Mohammad Gheshlaghi Azar; Yunhao Tang; Georg Ostrovski; Anna Harutyunyan; Karl Tuyls; Marc G. Bellemare; Will Dabney (J. Mach. Learn. Res., 2024)

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning. - Khimya Khetarpal; Zhaohan Daniel Guo; Bernardo Ávila Pires; Yunhao Tang; Clare Lyle; Mark Rowland 001; Nicolas Heess; Diana L. Borsa; Arthur Guez; Will Dabney (AISTATS, 2025)

Soft Policy Optimization: Online Off-Policy RL for Sequence Models. - Taco Cohen; David W. Zhang; Kunhao Zheng; Yunhao Tang; Rémi Munos; Gabriel Synnaeve (CoRR, 2025)

Optimizing Language Models for Inference Time Objectives using Reinforcement Learning. - Yunhao Tang; Kunhao Zheng; Gabriel Synnaeve; Rémi Munos (CoRR, 2025)

RL-finetuning LLMs from on- and off-policy data with a single algorithm. - Yunhao Tang; Taco Cohen; David W. Zhang; Michal Valko; Rémi Munos (CoRR, 2025)

Learning to chain-of-thought with Jensen's evidence lower bound. - Yunhao Tang; Sid Wang; Rémi Munos (CoRR, 2025)

LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training. - Bo Wu; Sid Wang; Yunhao Tang; Jia Ding; Eryk Helenowski; Liang Tan; Tengyu Xu; Tushar Gowda; Zhengxing Chen; Chen Zhu; Xiaocheng Tang; Yundi Qian; et al. (CoRR, 2025)

On a few pitfalls in KL divergence gradient estimation for RL. - Yunhao Tang; Rémi Munos (CoRR, 2025)

Magistral. - Abhinav Rastogi; Albert Q. Jiang; Andy Lo; Gabrielle Berrada; Guillaume Lample; Jason Rute; Joep Barmentlo; Karmesh Yadav; Kartik Khandelwal; Khyathi Raghavi Chandu; Léonard Blier; Lucile Saulnier; et al. (CoRR, 2025)

Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards. - Charles Arnal; Gaëtan Narozniak; Vivien Cabannes; Yunhao Tang; Julia Kempe; Rémi Munos (CoRR, 2025)