Hugging Face Trending Papers July 13, 2026

Active Offline-to-Online Reinforcement Learning

Read the original on Hugging Face Trending Papers →

Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction. This offline-to-online RL (O2O-RL) paradigm is particularly promising in nonstationary domains where interaction is costly or potentially hazardous.

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 Hugging Face Trending Papers.

arXiv AI

Jul 14

Active Offline-to-Online Reinforcement Learning

arXiv:2607. 11720v1 Announce Type: cross Abstract: Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction.

By Alper Kamil Bozkurt, Shangtong Zhang, Yuichi Motai

reinforcement-learning fine-tuning

arXiv AI

Jul 7

The Three Regimes of Offline-to-Online Reinforcement Learning

arXiv:2510. 01460v4 Announce Type: replace-cross Abstract: Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online interactions for fine-tuning.

By Lu Li, Tianwei Ni, Yihao Sun, Pierre-Luc Bacon

reinforcement-learning fine-tuning

arXiv Machine Learning

Sep 15

JumpStart Your Policy Learning with Lessons from 160,000 Training Runs

arXiv:2609.13730v1 Announce Type: new Abstract: Reliable progress in offline policy learning depends on careful reporting, well-tuned baselines, and evaluation across diverse conditions. Prior work h...

By Nabil Omi, Eric Bae, Chung Yik Edward Yeung, Siddhartha Sen, Ali Farhadi

benchmarks

arXiv Machine Learning

Aug 24

Decoupling Policy Extraction for Offline Reinforcement Learning

arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...

By Xuyao Lin, Yixiang Shan, Jinru Duan, Tao Yang, Xinyu Zhao, Runyu Lei, Yiming Zhao, Jiaxin Fan, Zongbao Feng, Peng Jia

rag reinforcement-learning

arXiv Machine Learning

Jun 16

Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

arXiv:2502. 19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL).

By Yi Zhao, Aidan Scannell, Wenshuai Zhao, Yuxin Hou, Tianyu Cui, Le Chen, Dieter B\"uchler, Arno Solin, Juho Kannala, Joni Pajarinen

reinforcement-learning robotics fine-tuning

arXiv AI

Jul 23

Leveraging Offline Supervision for Efficient and Generalizable Reinforcement Learning in Large-Scale Vision-Language-Action Models

arXiv:2607. 19399v1 Announce Type: cross Abstract: It is commonly observed that online reinforcement learning (RL) produces better-performing strategies than offline methods across a broad range of performance measures.

By Dmitriy Poyarkov, Aleksei Staroverov, Aleksandr I. Panov

reinforcement-learning fine-tuning multimodal benchmarks

One email a morning, machine-written

Related stories

Active Offline-to-Online Reinforcement Learning

The Three Regimes of Offline-to-Online Reinforcement Learning

JumpStart Your Policy Learning with Lessons from 160,000 Training Runs

Decoupling Policy Extraction for Offline Reinforcement Learning

Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data

Leveraging Offline Supervision for Efficient and Generalizable Reinforcement Learning in Large-Scale Vision-Language-Action Models