Cover Image

Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Ruijia, Zeng, Siliang, Li, Chenliang, Garcia, Alfredo, Hong, Mingyi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
by: Zeng, Siliang, et al.
Published: (2023)

A Bayesian Approach to Robust Inverse Reinforcement Learning
by: Wei, Ran, et al.
Published: (2023)

Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
by: Zeng, Siliang, et al.
Published: (2022)

Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach
by: Zhang, Xinnan, et al.
Published: (2025)

Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Reward Design
by: Wei, Quan, et al.
Published: (2025)

Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization
by: Li, Chenliang, et al.
Published: (2025)

Analysis of Overparameterization in Continual Learning under a Linear Model
by: Goldfarb, Daniel, et al.
Published: (2025)

Non-Asymptotic Optimization and Generalization Bounds for Stochastic Gauss-Newton in Overparameterized Models
by: Cayci, Semih
Published: (2025)

Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
by: Zhang, Gavin, et al.
Published: (2022)

Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
by: Li, Jiaxiang, et al.
Published: (2024)

Precise Asymptotic Generalization for Multiclass Classification with Overparameterized Linear Models
by: Wu, David X., et al.
Published: (2023)

From Demonstrations to Rewards: Alignment Without Explicit Human Preferences
by: Zeng, Siliang, et al.
Published: (2025)

Machine Unlearning under Overparameterization
by: Block, Jacob L., et al.
Published: (2025)

ADARL: Adaptive Low-Rank Structures for Robust Policy Learning under Uncertainty
by: Li, Chenliang, et al.
Published: (2025)

More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory
by: Simon, James B., et al.
Published: (2023)

Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment
by: Li, Chenliang, et al.
Published: (2024)

HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents
by: Peng, Jiangweizhi, et al.
Published: (2026)

Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning
by: Patil, Pratik, et al.
Published: (2024)

Is Optimal Transport Necessary for Inverse Reinforcement Learning?
by: Dong, Zixuan, et al.
Published: (2025)

Asymptotic Smoothing of the Lipschitz Loss Landscape in Overparameterized One-Hidden-Layer ReLU Networks
by: Baturin, Saveliy
Published: (2026)

Geometry and Local Recovery of Global Minima of Two-layer Neural Networks at Overparameterization
by: Zhang, Leyang, et al.
Published: (2023)

Learning in Stackelberg Mean Field Games: A Non-Asymptotic Analysis
by: Zeng, Sihan, et al.
Published: (2025)

How Much Training Data is Memorized in Overparameterized Autoencoders? An Inverse Problem Perspective on Memorization Evaluation
by: Abitbul, Koren, et al.
Published: (2023)

Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime
by: Zhang, Richard Y.
Published: (2021)

Improved Global Guarantees for the Nonconvex Burer--Monteiro Factorization via Rank Overparameterization
by: Zhang, Richard Y.
Published: (2022)

Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards
by: Topper, Noah, et al.
Published: (2024)

The Nuclear Route: Sharp Asymptotics of ERM in Overparameterized Quadratic Networks
by: Erba, Vittorio, et al.
Published: (2025)

How Does Overparameterization Affect Features?
by: Duzgun, Ahmet Cagri, et al.
Published: (2024)

An Analytical Model for Overparameterized Learning Under Class Imbalance
by: Mor, Eliav, et al.
Published: (2025)

Vulnerability Analysis of Safe Reinforcement Learning via Inverse Constrained Reinforcement Learning
by: Fan, Jialiang, et al.
Published: (2026)

Can Optimal Transport Improve Federated Inverse Reinforcement Learning?
by: Millard, David, et al.
Published: (2026)

Inverse Reinforcement Learning from Non-Stationary Learning Agents
by: Sivakumar, Kavinayan P., et al.
Published: (2024)

Non-Asymptotic Global Convergence of PPO-Clip
by: Liu, Yin, et al.
Published: (2025)

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit
by: Riedl, Konstantin, et al.
Published: (2026)

Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics
by: Kwon, Soo Min, et al.
Published: (2023)

Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization
by: Zhang, Yaoyu, et al.
Published: (2024)

On the Robustness of Distribution Support under Diffusion Guidance
by: Cao, Ruijia, et al.
Published: (2026)

Inverse Reinforcement Learning without Reinforcement Learning
by: Swamy, Gokul, et al.
Published: (2023)

The Interpolating Information Criterion for Overparameterized Models
by: Hodgkinson, Liam, et al.
Published: (2023)

Theoretical Limitations of Ensembles in the Age of Overparameterization
by: Dern, Niclas, et al.
Published: (2024)