arXiv AI August 3, 2026 By Naman Saxena, Mudit Gaur, Vaneet Aggarwal

Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity

arXiv:2607. 28849v1 Announce Type: cross Abstract: Bilevel reinforcement learning (RL) is an important framework within the literature of RL that can be used to formalize various categories of problems, such as meta-learning, hierarchical task decomposition, and reinforcement learning from human feedback (RL-HF).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 12

Efficient Hypergradient Descent for Inverse Reinforcement Learning

arXiv:2608. 11052v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) aims to recover a reward function under which the resulting policy reproduces the behavior observed in expert demonstrations.

By Nikita Sevriukov, Anna Barabanova, Uliana Gagarina, Karina Ivanova, Sofiia Kasaeva, Ilya Levin, Marina Sheshukova

reinforcement-learning

arXiv Machine Learning

Jul 17

Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration

arXiv:2310. 07211v2 Announce Type: replace Abstract: Regularization is a cornerstone of modern reinforcement learning.

By Zeyang Li, Chuxiong Hu, Yunan Wang, Guojian Zhan, Jie Li, Yao Lyu, Shengbo Eben Li

reinforcement-learning

arXiv Machine Learning

Jun 16

A Fully First-Order Layer for Differentiable Optimization

arXiv:2512. 02494v2 Announce Type: replace Abstract: Differentiable optimization layers enable learning systems to make decisions by solving embedded optimization problems.

By Zihao Zhao, Kai-Chia Mo, Shing-Hei Ho, Brandon Amos, Kai Wang

arXiv Machine Learning

Sep 24

Value Mirror Descent for Reinforcement Learning

arXiv:2604. 06039v2 Announce Type: replace-cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL).

By Zhichao Jia, Guanghui Lan

reinforcement-learning

arXiv AI

Jun 11

Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning

arXiv:2603. 14867v4 Announce Type: replace-cross Abstract: Many strategic decision-making problems, such as environment design for warehouse robots, can be naturally formulated as bi-level reinforcement learning (RL), where a leader agent optimizes its objective while a follower solves a Markov decision process (MDP) conditioned on the leader's decisions.

By Mikoto Kudo, Takumi Tanabe, Akifumi Wachi, Youhei Akimoto

agents reinforcement-learning robotics

arXiv Machine Learning

Jul 24

Non-Stationary Functional Bilevel Optimization

arXiv:2601. 15363v2 Announce Type: replace-cross Abstract: Functional bilevel optimization (FBO) provides a powerful framework for hierarchical learning in function spaces, yet current methods are limited to static offline settings and perform suboptimally in online, non-stationary scenarios.

By Jason Bohne, Ieva Petrulionyte, Michael Arbel, Julien Mairal, Pawe{\l} Polak

reinforcement-learning

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Efficient Hypergradient Descent for Inverse Reinforcement Learning

Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration

A Fully First-Order Layer for Differentiable Optimization

Value Mirror Descent for Reinforcement Learning

Sample-Efficient Hypergradient Estimation for Decentralized Bi-Level Reinforcement Learning

Non-Stationary Functional Bilevel Optimization