arXiv Machine Learning September 22, 2026 By Ali Beikmohammadi, Sarit Khirirat, Sindri Magn\'usson

Personalized Federated Reinforcement Learning via Model-Agnostic Meta-Learning: Convergence of Exact and Hessian-Free Meta-Policy Gradients

Read the original on arXiv Machine Learning →

arXiv:2609. 22833v1 Announce Type: new Abstract: We study personalized federated reinforcement learning, in which $n$ agents, each acting in its own Markov decision process, collaborate through a server to learn a shared MAML-style policy initialization that becomes effective for an individual agent once that agent adapts it with a single local policy-gradient step.

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 Machine Learning.

arXiv Machine Learning

4d ago

Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness

arXiv:2609.36945v1 Announce Type: new Abstract: We study the learning dynamics of fine-tuning a policy model on self-generated and reward-weighted data, with particular focus on a generalized version...

By Zhiwei Wang, Yanxi Chen, Yaliang Li, Bolin Ding

llms reinforcement-learning fine-tuning

arXiv AI

Jul 22

From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning

arXiv:2607. 18830v1 Announce Type: cross Abstract: Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks.

By Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi

rag reinforcement-learning benchmarks

arXiv Machine Learning

Aug 13

A New First-Order Meta-Learning Algorithm with Convergence Guarantees

arXiv:2409. 03682v2 Announce Type: replace Abstract: Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems.

By El Mahdi Chayti, Martin Jaggi

llms efficiency safety

arXiv Machine Learning

2d ago

Fast Regularized Policy Mirror Descent with One-Step TD Updates

arXiv:2609.39837v1 Announce Type: new Abstract: Policy mirror descent (PMD) enjoys fast convergence in regularized Markov decision processes (MDPs), but existing guarantees often rely on exact or inc...

By Qipei Chen, Wenye Li, Yule Sun, Ke Wei

reinforcement-learning

arXiv Machine Learning

Aug 21

Unregularized Convergence of Single-Loop, Entropy-Regularized Natural Actor-Critic

arXiv:2608. 19587v1 Announce Type: new Abstract: While entropy regularization is widely used to stabilize and accelerate Natural Policy Gradient methods, its ability to yield faster convergence rates for the unregularized objective remains underexplored.

By Zhiqiang Tan

reinforcement-learning

Hugging Face Trending Papers

Aug 20

Unregularized Convergence of Single-Loop, Entropy-Regularized Natural Actor-Critic

While entropy regularization is widely used to stabilize and accelerate Natural Policy Gradient methods, its ability to yield faster convergence rates for the unregularized objective remains underexplored. Existing analyses often rely on double-loop architectures and invoke a linear entropy penalty.

reinforcement-learning

One email a morning, machine-written

Related stories

Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness

From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning

A New First-Order Meta-Learning Algorithm with Convergence Guarantees

Fast Regularized Policy Mirror Descent with One-Step TD Updates

Unregularized Convergence of Single-Loop, Entropy-Regularized Natural Actor-Critic

Unregularized Convergence of Single-Loop, Entropy-Regularized Natural Actor-Critic