Yaodong Yang
助理教授
Researches reinforcement learning, multi-agent systems, AI alignment and embodied intelligence.
Yaodong Yang's lab at Peking works on reinforcement learning, trustworthy & safe ML and planning, control & multi-robot systems. 137 papers since 2023, 105 at top venues, mostly NeurIPS and ICML. 5 PhD students and 1 master's student. Applications close .
At a glance
- papers, 2023–2026
- 36/ year
- at T1 venues
- 77%
- PhD students
- 5
- MS students
- 1
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 45 papers2026 · T1 35 · collecting
- 41 papers2025 · T1 30
- 26 papers2024 · T1 21
- 25 papers2023 · T1 19
T1 venuesOtherVenues: NeurIPS, ICML, ICLR, AAAI, Findings of ACL
Research focuspaper titles that use each topic's words
- Reinforcement learning53 papers
- Planning, control & multi-robot systems32 papers
- Trustworthy & safe ML8 papers
- researches reinforcement0 papers
- intelligence0 papers
Paper list
From the lab homepage.
202645 papers
- COLMA Blind Spot in Alignment: Quantifying Biosecurity Risks in Large Language Models *
- ACL (T1)A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus *
- NeurIPS (T1)AdaMAP: Learning Adaptive Multi-Action Prediction with Grounded Dreaming Guidance
- OpenReviewAdversarial Policy Transfer in Mixed Cooperative-Competitive Games
- ICLR (T1)Align Once, Benefit Multilingually: Enforcing Multilingual Consistency for LLM Safety Alignment *
- ICML (T1)Are Your Agents Upward Deceivers?
- AAMAS (T1)Beyond Self-Interest: Modeling Social-Oriented Motivation for Human-like Multi-Agent Interactions
- AAAI (T1)CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation
- ICRA (T1)CDF-Glove: A Cable-Driven Force Feedback Glove for Dexterous Teleoperation
- ACL (T1)Communication-Efficient Desire Alignment for Proactive Embodied Human–Agent Interaction * (opens in a new tab)
- NeurIPS (T1)CURE: Counterfactual Unsafe-token Re-masking for Diffusion Large Language Model Test-time Alignment *
- ICML (T1)Debate with Images: Detecting Deceptive Behaviors in Multimodal Large Language Models *
202541 papers
- CASEA Unified Framework for Multi-Stage Decision Optimization with Deep Reinforcement Learning and Foundation Models * (opens in a new tab)
- IROS (T1)Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation
- ICLR (T1)Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs *
- Findings of ACLBenchmarking Multi-National Value Alignment for Large Language Models
- Findings of ACLBoosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (opens in a new tab)
- CoRL (T1)ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
- NeurIPS (T1)DexFlyWheel: A Scalable and Self-improving Data Generation Framework for Dexterous Manipulation *
- AAAI (T1)Differentiable Information Enhanced Model-Based Reinforcement Learning *
- ICLR (T1)Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
- NeurIPS (T1)Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning *
- EMNLP (T1)Enhancing LLM-Based Social Bot via an Adversarial Learning Framework
- ICML (T1)Falcon: Fast Visuomotor Policies via Partial Denoising *
202426 papers
- AAAI (T1)A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning *
- NeurIPS (T1)Aligner: Efficient Alignment by Learning to Correct *
- CVPR (T1)AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents
- ICLR (T1)Byzantine Robust Cooperative Multi-Agent Reinforcement Learning as a Bayesian Game *
- ICLR (T1)CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
- ICASSP (T1)Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
- ICML (T1)Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning
- ICML (T1)End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
- NeurIPS WorkshopFPGA-Gym: An FPGA-Accelerated Reinforcement Learning Environment Simulation Framework
- ICLR (T1)Maximum Entropy Heterogeneous-Agent Reinforcement Learning *
- CoRL (T1)Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping
- CoRL (T1)Object-Centric Dexterous Manipulation from Human Motion Data
202325 papers
- ICML (T1)A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
- OpenReviewA Massively Parallel Benchmark for Safe Dexterous Manipulation *
- AAAI (T1)ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency *
- NeurIPS (T1)BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset *
- OpenReviewConvergence Rate of Primal-Dual Approach to Constrained Reinforcement Learning with Softmax Policy
- CoRL (T1)Dynamic Handover: Throw and Catch with Bimanual Hands
- ICML (T1)GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
- ICRA (T1)GenDexGrasp: Generalizable Dexterous Grasping
- NeurIPS (T1)Hierarchical Multi-Agent Skill Discovery *
- AAMAS (T1)Is Nash Equilibrium Approximator Learnable?
- OpenReviewJoint-predictive representations for multi-agent reinforcement learning
- AAAI (T1)Learning to Shape Rewards using a Game of Two Partners *
Where alumni went
Alumni are not listed on the lab homepage.
Apply to PekingFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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