Natasha Jaques
Assistant Professor
Develops social reinforcement learning, combining multi-agent training and human feedback to improve AI agents' coordination and generalization.
Natasha Jaques's lab at UW works on reinforcement learning, learning theory & optimization and planning, control & multi-robot systems. 29 papers since 2023. 9 PhD students and 1 master's student. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 0%
- PhD students
- 9
- MS students
- 1
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 12 papers2026 · T1 0 · collecting
- 11 papers2025 · T1 0
- 6 papers2024 · T1 0
- 0 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Reinforcement learning12 papers
- Learning theory & optimization12 papers
- Planning, control & multi-robot systems8 papers
- coordination2 papers
- human feedback2 papers
Paper list
From the lab homepage.
202612 papers
- International Conference on Learning Representations (ICLR)AutoCode: LLMs as Problem Setters for Competitive Programming (opens in a new tab)
- International Conference on Machine Learning (ICML) @ COLM AIA WorkshopChasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models (opens in a new tab)
- International Conference on Learning Representations (ICLR)Generative Adversarial Post-Training Mitigates Reward Hacking in Live Human-AI Music Interaction (opens in a new tab)
- International Conference on Learning Representations (ICLR)Improving Human-AI Coordination through Online Adversarial Training and Generative Models (opens in a new tab)
- International Conference on Learning Representations (ICLR)Learning to Summarize User Information for Personalized Reinforcement Learning from Human Feedback (opens in a new tab)
- International Conference on Machine Learning (ICML)LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning (opens in a new tab)
- International Conference on Machine Learning (ICML)Maximizing Mutual Information Between Prompt and Response Improves LLM Performance with No Additional Data (opens in a new tab)
- International Conference on Learning Representations (ICLR) @ NeurIPS LAW WorkshopModeling Others' Minds as Code (opens in a new tab)
- Empirical Methods in Natural Language Processing (EMNLP) FindingsPigeonholing: How Bad Prompts Hurt Models, Causing Collapse and Mistakes (opens in a new tab)
- International Conference on Machine Learning (ICML)Position: Solipsistic Superintelligence Is Unlikely to Be Cooperative (opens in a new tab)
- International Conference on Machine Learning (ICML)RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments (opens in a new tab)
- International Conference on Learning Representations (ICLR)SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning (opens in a new tab)
202511 papers
- IEEE International Conference on Robotics and Automation (ICRA)Achieving Human Level Competitive Robot Table Tennis (opens in a new tab)
- NeurIPS Open World Agents WorkshopAn Efficient Open World Benchmark for Multi-Agent Reinforcement Learning (opens in a new tab)
- Neural Information Processing Systems (NeurIPS)Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning (opens in a new tab)
- International Conference on Machine Learning (ICML) and CogSciCross-environment Cooperation Enables Zero-shot Multi-agent Coordination (opens in a new tab)
- Neural Information Processing Systems (NeurIPS) @ Paul G. Allen School of Computer Science & EngineeringEnhancing Personalized Multi-Turn Dialogue with Curiosity Reward (opens in a new tab)
- Neural Information Processing Systems (NeurIPS)Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia (opens in a new tab)
- NeurIPS MATH.AI WorkshopGenerative Modeling for Robust Deep Reinforcement Learning on the Traveling Salesman Problem (opens in a new tab)
- AAAI Conference on Artificial Intelligence (AAAI)Infer Human’s Intentions Before Following Natural Language Instructions (opens in a new tab)
- International Conference on Learning Representations (ICLR)InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma (opens in a new tab)
- AAAI Conference on Artificial Intelligence (AAAI)Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems (opens in a new tab)
- Extended Abstracts of The ACM Conference on Human Factors in Computing Systems (CHI)ReaLJam: Real-Time, Synchronous Human-AI Music Jamming with Reinforcement Learning-Tuned Transformers (opens in a new tab)
20246 papers
- International Conference on Machine Learning (ICML)Adaptive Accompaniment with ReaLchords (opens in a new tab)
- Proceedings of the National Academy of Sciences (PNAS)Impossibility theorems for feature attribution (opens in a new tab)
- Neural Information Processing Systems (NeurIPS)Learning to Cooperate with Humans Using Generative Agents (opens in a new tab)
- Empirical Methods in Natural Language Processing (EMNLP)Moral Foundations of Large Language Models (opens in a new tab)
- Neural Information Processing Systems (NeurIPS)Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning (opens in a new tab)
- Neural Information Processing Systems (NeurIPS) Competition TrackThe Concordia Contest: Advancing the Cooperative Intelligence of Language Agents (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UWFall 2027
UW Allen School Computer Science PhD
- Round
- Single round
- Opens
- GRE
- not required
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
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