arXiv Machine Learning
Jul 21
Theoretical Foundations of $\max$@$k$ Reinforcement Learning
arXiv:2607. 17823v1 Announce Type: new Abstract: Reinforcement Learning is a cornerstone technique for modern large reasoning models.
A Hierarchy of Policy Learning Problems
arXiv:2607. 03385v1 Announce Type: cross Abstract: Policy learning has received substantial attention with the goal of learning policies from observational data for decision-making.
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arXiv Machine Learning
Aug 10
Learning Suffers More Than the Policy Class Under Partial Observability: A Closed-Form Analysis
arXiv:2608. 07228v1 Announce Type: new Abstract: When a reinforcement learning agent cannot observe the full state, we usually blame its policies: it cannot see enough to represent a good one.
arXiv Machine Learning
Aug 11
Regret, equilibrium, and learning in games: A guided tour
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
arXiv Machine Learning
Jun 2
Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear $q^\pi$-Realizability and Concentrability
arXiv:2510. 03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization.
arXiv Machine Learning
1d ago
Towards Optimal Policy Improvement
The paper introduces a framework for optimal policy improvement in reinforcement learning, defining it as the best single update under given constraints. It shows that restricting improvement to a subset of states is equivalent to solving an induced Markov Decision Process, linking planning with explicit or implicit models to optimal policy improvement. The authors develop a novel operator for greedification under approximate evaluation, demonstrating empirical gains across several RL algorithms and settings.
arXiv AI
Jul 1
Behavior Cloning is Not All You Need: The Optimality of On-Policy Distillation for Noisy Expert Feedback
arXiv:2606. 30923v1 Announce Type: cross Abstract: Imitation Learning is a natural framework for learning in sequential decision-making systems and has emerged as the dominant paradigm through which we understand language model training.
arXiv Machine Learning
Sep 18
Stable Policy Learning
The paper investigates how policy learning algorithms should balance expected welfare against sampling risk in evidence-based policymaking. It demonstrates that algorithmic stability—specifically, a policy’s insensitivity to the replacement of a single experimental unit—limits sampling risk. The authors introduce policy‑vote bagging, which trains on many subsamples and averages their votes, preserving expected welfare while improving expected utility for risk‑averse researchers, and provide sharp bounds linking estimation accuracy, subsample size, and welfare variation, including an exact guarantee under CARA utility.
arXiv Machine Learning
Jun 16
On the Role of Computation in Reinforcement Learning
arXiv:2602. 05999v3 Announce Type: replace Abstract: How does the amount of compute available to a reinforcement learning (RL) policy affect its learning?
arXiv AI
Jun 9
An Agency-Transferring Model-Free Policy Enhancement Technique
arXiv:2606. 09825v1 Announce Type: cross Abstract: Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substantial computation.
arXiv AI
Jun 2
Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
arXiv:2606. 00367v1 Announce Type: cross Abstract: Reinforcement learning problems typically define the goal as maximizing the expected value of a scalar reward function.
arXiv Machine Learning
Jul 31
On-Policy and Off-Policy Learning for Large Action Spaces
arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.
arXiv Machine Learning
Aug 28
Algorithmic Principles For Multiclass Learning Are Hard To Come By: Limits of Regularization and Proper Learning
The paper investigates fundamental limits of algorithmic principles in multiclass learning, specifically proper learning and regularization. It shows that learning cannot always be reduced to proper learning even with an enlarged hypothesis class, that proper learners may need a sublinear number of errors that can be arbitrarily large, and that regularization (SRM or local) is not universally sufficient. The authors also provide a positive theory giving sufficient conditions for SRM learnability and a characterization via integrability of revealed preferences.