NeurIPS 2025PastSafety & alignmentReinforcement learningTheory
NeurIPS 2025 Workshop: Second Workshop on Aligning Reinforcement Learning Experimentalists and Theorists
ARLET
- Submission deadline
- Sep 3, 2025, 13:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (101)
Fetched from OpenReview (v2) on 2026-06-10.
A Regularized Actor-Critic Algorithm for Bi-Level Reinforcement Learning
A Reinforcement Learning Approach for Health-Behavioural Recommendations to Reduce Cancer Risk
A Theoretical Analysis of Information Bottlenecks for Zero-Shot Transfer in Reinforcement Learning
Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation
Active Learning for Stochastic Contextual Linear Bandits
Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making
All Roads Lead to Likelihood: The Value of RL in Fine-Tuning
Automatic Reward Shaping from Multi-Objective Human Heuristics
Bandit and Delayed Feedback in Online Structured Prediction
Bandit Learning on Dynamic Graphs
Behavior-Aware Off-Policy Selection in High-Stake Human-Centric Environments
Beyond Marginals: Capturing Correlated Returns through Joint Distributional Reinforcement Learning
Beyond RLHF and NLHF: Population-Proportional Alignment under an Axiomatic Framework
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners
Bootstrap Ensemble Uncertainty for State-Adaptive Regularization in Offline Reinforcement Learning
Compute-Optimal Scaling for Value-Based Deep RL
Constrained Linear Thompson Sampling
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
Data-Dependent Regret Bounds for MABs with Constraints
Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL
DHP: Discrete Hierarchical Planning for HRL Agents
Efficient Adversarial Attacks on High-dimensional Offline Bandits
Efficient Restarts in Non-Stationary Model-Free Reinforcement Learning
Efficiently Robust In-Context Reinforcement Learning with Adversarial Generalization and Adaptation
Enhancing Diversity in Large Language Models via Determinantal Point Processes
Exploration Implies Data Augmentation: Reachability and Generalisation in Contextual MDPs
Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatment
Fictive Learning Augments Model-Based Reinforcement Learning in the Two-Step Task
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
From Contextual Combinatorial Semi-Bandits to Bandit List Classification: Improved Sample Complexity with Sparse Rewards
Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update
Generating Auxiliary Tasks with Reinforcement Learning
Horizon Reduction Makes RL Scalable
How to Provably Improve Return Conditioned Supervised Learning?
Human-Inspired Multi-Level Reinforcement Learning
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning
Idea: Bridging Theoretical Fairness Definitions with Multi-Agent Coordination in the Real World
Idea: Fairness Constraints as Reliability Guarantees for RLHF Reward Models
Idea: Sharpe Ratio-Optimized Thompson Sampling for Risk-Aware Online Learning
Improved Regret Bounds for Linear Bandits with Heavy-Tailed Rewards
Improved Training Mechanisms for Reinforcement Learning via Online Model Selection
Improving Value Estimation Critically Enhances Vanilla Policy Gradient
Intent‑Based Reward Inference for Value‑Aligned Reinforcement Learning
Large Language Model-Enhanced RL for Diverse and Novel Recommendations
Learning a Pessimistic Reward in RLHF: KL Regularization is Not Necessary
Linear Dynamics meets Linear MDPs: Closed-Form Optimal Policies via Reinforcement Learning
LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning
Long-Horizon Model-Based Offline Reinforcement Learning Without Conservatism
MOBODY: Model-Based Off-Dynamics Offline Reinforcement Learning
On the relation of bisimulation, model irrelevance, and corresponding regret bounds
Open Problem: Order Optimal Regret Bounds for Non-Markovian Rewards
Optimal Regret Bounds for Policy Optimization in Contextual Bandits
Optimistic Actor-Critic with Parametric Policies: Unifying Sample Efficiency and Practicality
Oracle-RLAIF: An Improved Fine-Tuning Framework for Multi-modal Video Models through Reinforcement Learning from Ranking Feedback
Outcome-based Exploration for LLM Reasoning
Policy Compatible Skill Incremental Learning via Lazy Learning Interface
Policy Gradient Guidance Enables Test Time Control
Policy Optimization in CMDPs with Bandit Feedback: Learning with Stochastic and Adversarial Constraints
Policy Search via Bayesian Optimization with Temporal Difference Gaussian Processes
Policy Testing in Markov Decision Processes
Principled Learning-to-Communicate in Cooperative MARL: An Information-Structure Perspective
Provably Efficient and Agile Randomized Q-Learning
Quantile Reward Policy Optimization: Alignment with Pointwise Regression and Exact Partition Functions
Real-World Reinforcement Learning of Active Perception Behaviors
Regret Bounds for Adversarial Contextual Bandits with General Function Approximation and Delayed Feedback
Replicable Reinforcement Learning with Linear Function Approximation
Revisiting Actor-Critic Methods in Discrete Action Off-Policy Reinforcement Learning
Revisiting Mixture Policies in Entropy-Regularized Actor-Critic
Reward Model Overoptimisation in Iterated RLHF
RL's Razor: Why On-Policy Reinforcement Learning Forgets Less
Robust Constrained Offline Reinforcement Learning with Linear Function Approximation
Robust Policy Gradient Optimization through Parameter Perturbation in Reinforcement Learning
Safe Exploration via Policy Priors
Safe Guaranteed Dynamics Exploration with Probabilistic Models
Safe, Trust Region Policy Optimization for Constrained Reinforcement Learning
Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking
Scaling Offline RL via Efficient and Expressive Shortcut Models
Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs
Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards
Speaking the Language of Teamwork: LLM-Guided Credit Assignment in Multi-Agent Reinforcement Learning
Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning
Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
State Entropy Regularization for Robust Reinforcement Learning
Steering Diffusion Policies with Value-Guided Denoising
Structure Matters: Dynamic Policy Gradient
SUSD: Structured Unsupervised Skill Discovery through State Factorization
TARC: Time-Adaptive Robotic Control
Test Time Risk Adaption with Mixture of Agents
The Good, The Bad, and The Hybrid: A Reward Structure Showdown in Reasoning Models Training
The Minimax Complexity of Preference-Based Decision Making in Multi-Objective Reinforcement Learning
The Role of Preference Data and Unembeddings in the Convergence Rate of DPO
Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
Towards Parameter-Free Temporal Difference Learning
Towards shutdownable agents via stochastic choice
Uncertainty-Aware Policy-Preserving Abstractions with Abstention for One-Shot Decisions
Unifying Agent Interaction and World Information for Multi-agent Coordination
Unsupervised Contrastive Goal Reaching
What Makes a Reward Model a Good Teacher? An Optimization Perspective
When Data is the Algorithm: A Systematic Study and Curation of Preference Optimization Datasets
When Maximum Entropy Misleads Policy Optimization