Nan Jiang
Associate Professor
Studies reinforcement learning theory, including sample-efficient exploration and offline policy evaluation.
Nan Jiang's lab at UIUC works on learning theory & optimization, reinforcement learning and efficient training & inference. 34 papers since 2023, 31 at top venues, mostly NeurIPS and ICLR. 6 PhD students. 14 alumni, most often to ByteDance and OpenAI. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 91%
- PhD students
- 6
- MS students
- 0
- alumni listed
- 14
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 5 · collecting
- 9 papers2025 · T1 8
- 11 papers2024 · T1 10
- 8 papers2023 · T1 8
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, ACL, CoRL
Research focuspaper titles that use each topic's words
- Reinforcement learning17 papers
- Learning theory & optimization15 papers
- policy evaluation7 papers
- Efficient training & inference1 paper
- sample-efficient0 papers
Paper list
From the lab homepage.
20266 papers
- ICLR (T1)A Unifying View of Coverage in Linear Off-Policy Evaluation
- NeurIPS (T1)Beyond Pessimism: Offline Learning in KL-regularized Games
- ICML (T1)Offline Two-Player Zero-Sum Markov Games with KL Regularization
- ICLR (T1)On the Tension Between Optimality and Adversarial Robustness in Policy Optimization
- NeurIPS (T1)Q-MMR: Off-Policy Evaluation via Recursive Reweighting and Moment Matching
- COLMReinforce-Ada: An Adaptive Sampling Framework under Non-linear RL Objectives
20259 papers
- NeurIPS (T1)A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning
- NeurIPS (T1)Improving LLM General Preference Alignment via Optimistic Online Mirror Descent
- ICML (T1)Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
- ICLR (T1)Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
- NeurIPS (T1)Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol
- STSOffline Reinforcement Learning in Large State Spaces: Algorithms and Guarantees
- NeurIPS (T1)Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL
- ICLR (T1)Statistical Tractability of Off-policy Evaluation of History-dependent Policies in POMDPs
- NeurIPS (T1)Thinking vs. Doing: Improving Agent Reasoning by Scaling Test-Time Interaction
202411 papers
- ICLR (T1)Harnessing Density Ratios for Online Reinforcement Learning
- ICML (T1)Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint
- EMNLP (T1)Mitigating the Alignment Tax of RLHF
- JMLR (T1)Model-free Representation Learning and Exploration in Low-rank MDPs
- RLCNon-adaptive Online Finetuning for Offline Reinforcement Learning
- NeurIPS (T1)Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality
- NeurIPS (T1)On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation
- NeurIPS (T1)Online Iterative Reinforcement Learning from Human Feedback with General Preference Model
- NeurIPS (T1)Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity
- TMLR (T1)RLHF Workflow: From Reward Modeling to Online RLHF
- ACL (T1)Word Embeddings Are Steers for Language Models
20238 papers
- NeurIPS (T1)Adversarial Model for Offline Reinforcement Learning
- ICLR (T1)Explaining RL Decisions with Trajectories
- NeurIPS (T1)Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
- CoRL (T1)Marginalized Importance Sampling for Off-Environment Policy Evaluation
- ICML (T1)Offline Learning in Markov Games with General Function Approximation
- ICML (T1)Reinforcement Learning in Low-Rank MDPs with Density Features
- ICML (T1)The Optimal Approximation Ratios in Misspecified Off-Policy Value Function Estimation
- ICLR (T1)The Role of Coverage in Online Reinforcement Learning
Where alumni went14 listed, 40% to industry
Industry 2Academia 3
Most often to
- ByteDance
- OpenAI
Apply to UIUCFall 2027
UIUC Siebel Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL iBT / IELTS
- Fee
- USD 90
Not yet confirmed for this intake.
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