Off2OnRL-awesome-papers [Offline Online Reinforcement Learning]

This is a collection of research and review papers for Offline to Online Reinforcement Learning (RL) (or Offline Online RL). If you find this repository helpful, please consider citing our survey and giving a ⭐!

Maintainers:

• Linh LPV (A2I2, Deakin University)

Please feel free to pull request with the instructions provided in Contributing.

Format:
- [title](paper linnk) [links]
    - author 1, author 2, et al. arXiv/conferences/journals, month/year.

For any questions, feel free to contact: lephamvanlinh@gmail.com

Credit:

• The Contributing instruction is based on the workflow of awesome-exploration-rl .

📑 Citation

@article{le2026offline2onlinerl,
  title={Awesome Offline to Online RL Papers},
  author={Linh Le},
  journal={https://github.com/linhlpv/awesome-offline-to-online-RL-papers},
  year={2026}
}

📋 Table of Contents

• Off2OnRL-awesome-papers [Offline Online Reinforcement Learning]
  • 📑 Citation
  • 📋 Table of Contents
  • Papers
    • Preprints
    • ICLR 2026
    • Submitted ICLR 2026
    • 2025
      • AAMAS 2025
      • NeurIPS 2025
      • ICML 2025
      • IJCAI 2025
      • AAAI 2025
      • ICLR 2025
    • 2024
      • ICRA 2024
      • AAMAS 2024
      • NeurIPS 2024
      • ICML 2024
      • AAAI 2024
      • IJCAI 2024
      • RLC 2024
      • ICLR 2024
      • Underreviewed ICLR 24
    • 2023
      • NeurIPS 2023
      • ICML 2023
      • AAAI 2023
      • ICLR 2023
      • ICRA 2023
      • CORL 2023
    • 2022
      • NeurIPS 2022
      • ICML 2022
      • ICLR 2022
      • CORL 2022
      • RSS 2022
    • Multi-Agent Reinforcement Learning
    • Journal Papers
    • TMLR
  • ⭐ Star History

Papers

Preprints

• WOMBET: World Model-based Experience Transfer for Robust and Sample-efficient Reinforcement Learning

  • Mintae Kim, Koushil Sreenath. arXiv 2026. 04/2026. L4DC 2026.

• Offline-to-Online Reinforcement Learning with Prioritized Experience Selection

  • Zhongjian Qiao, Jiafei Lyu, Qi Liu, Xiu Li. Submitted to ICLR 2025.

• Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing

  • Wall Kim, Chaeyoung Song, Hanul Kim. arXiv 2026. 2/2026.

• Adversarial Fine-tuning in Offline-to-Online Reinforcement Learning for Robust Robot Control

  • Shingo Ayabe, Hiroshi Kera, Kazuhiko Kawamoto. arXiv 2025. 10/2025.

• MOORe: Model-based Offline-to-Online Reinforcement Learning

  • Yihuan Mao, Chao Wang, Bin Wang, Chongjie Zhang. arXiv 2022. 01/2022.

• Launchpad: Learning to Schedule Using Offline and Online RL Methods

  • Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi. arXiv. 12/2022.x

• Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration

  • Ziqi Zhang, Xiao Xiong, Zifeng Zhuang, Jinxin Liu, Donglin Wang. arXiv. 10/2023.

• Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration

  • Jinning Li, Xinyi Liu, Banghua Zhu, Jiantao Jiao, Masayoshi Tomizuka, Chen Tang, Wei Zhan. arXiv. 09/2023.

• Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness

  • Xiaoyu Wen, Xudong Yu, Rui Yang, Chenjia Bai, Zhen Wang. arXiv. 09/2023.

• Sample Efficient Reward Augmentation in offline-to-online Reinforcement Learning

  • Ziqi Zhang, Xiao Xiong, Zifeng Zhuang, Jinxin Liu, Donglin Wang. arXiv. 10/2023.

• AWAC: Accelerating Online Reinforcement Learning with Offline Datasets page

  • Ashvin Nair, Abhishek Gupta, Murtaza Dalal, Sergey Levine. arXiv. 06/2020.

• Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets

  • Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, Jinwoo Shin. Offline RL workshop. 12/2020.

• Launchpad: Learning to Schedule Using Offline and Online RL Methods

  • Vanamala Venkataswamy, Jake Grigsby, Andrew Grimshaw, Yanjun Qi. arXiv. 12/2022.

• Guiding Online Reinforcement Learning with Action-Free Offline Pretraining

  • Deyao Zhu, Yuhui Wang, Jürgen Schmidhuber, Mohamed Elhoseiny. arXiv. 01/2023.

• Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions

  • Yicheng Luo, Jackie Kay, Edward Grefenstette, Marc Peter Deisenroth. arXiv. 03/2023.

• PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning

  • Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu, Xianyuan Zhan, Ya-Qin Zhang. arXiv. 05/2023.

• Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic

  • Tianying Ji, Yu Luo, Fuchun Sun, Xianyuan Zhan, Jianwei Zhang, Huazhe Xu. arXiv. 06/2023.

• A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning

  • Siyuan Guo, Yanchao Sun, Jifeng Hu, Sili Huang, Hechang Chen, Haiyin Piao, Lichao Sun, Yi Chang. arXiv. 06/2023.

• Sample Efficient Offline-to-Online Reinforcement Learning

  • Siyuan Guo, Lixin Zou, et al. IEEE Transactions on Knowledge and Data Engineering ( Early Access ). 08/2023.

ICLR 2026

• Flow Matching with Injected Noise for Offline-to-Online Reinforcement Learning

  • Yongjae Shin, Jongseong Chae, Jongeui Park, Youngchul Sung. ICLR 2026.

• EXPO: Stable Reinforcement Learning with Expressive Policies

  • Perry Dong, Qiyang Li, Dorsa Sadigh, Chelsea Finn. ICLR 2026.

• Action-Free Offline-to-Online RL via Discretised State Policies

  • Natinael Solomon Neggatu, Jeremie Houssineau, Giovanni Montana. ICLR 2026.

Submitted ICLR 2026

• Tackling Heavy-Tailed Q-Value Bias in Offline-to-Online Reinforcement Learning with Laplace-Robust Modeling

• On-policy Reinforcement Fine-tuning with Offline reward for Multi-step Embodied Planning

• The Three Regimes of Offline-to-Online Reinforcement Learning

• Towards Optimism-Pessimism Trade-off in Model-based Offline-to-Online Reinforcement Learning

• Action-Free Offline-To-Online RL via Discretised State Policies

• Exploration for Deployment-Efficient Reinforcement Learning Agents

• Annealing Bridges Offline and Online RL

• EXPO: Stable Reinforcement Learning with Expressive Policies

• From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Release for Offline-to-Online RL

• Trajectory Generation for Offline-to-Online Reinforcement Learning via Entropy Perspective

• How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning

• SAMG: Offline-to-Online Reinforcement Learning via State-Action-Conditional Offline Model Guidance

• Explicitly Bounding Q‑Function Estimates for Offline-to-Online Reinforcement Learning

• Flow Matching with Injected Noise for Offline-to-Online Reinforcement Learning

• Efficient Zero-Shot Coordination via Offline Policy Diversity and Online Belief Reasoning

• Enhancing Offline-to-Online Reinforcement Learning by Adaptive Experience Aligned Diffusion Sampling

• Bridging Offline and Online Reinforcement Learning for LLMs

• Online Finetuning Decision Transformers with Policy Gradients

• Generative Actor Critic

2025

AAMAS 2025

• Adaptive Offline Data Replay in Offline-to-Online Reinforcement Learning
  • Xu Liu, Tong Yu, Shuai Li. AAMAS 2025 Extended Abstract.

NeurIPS 2025

• Robust Policy Expansion for Offline-to-Online RL under Diverse Data Corruption
  • Longxiang He, Deheng Ye, Junbo Tan, Xueqian Wang, Li Shen. NeurIPS 2025.
• Uni-RL: Unifying Online and Offline RL via Implicit Value Regularization
  • Haoran Xu, Liyuan Mao, Hui Jin, Weinan Zhang, Xianyuan Zhan, Amy Zhang. NeurIPS 2025.
• Reinforcement Learning with Action Chunking
  • Qiyang Li, Zhiyuan Zhou, Sergey Levine. NeurIPS 2025.

ICML 2025

• Penalizing Infeasible Actions and Reward Scaling in Reinforcement Learning with Offline Data

  • Jeonghye Kim, Yongjae Shin, Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Youngchul Sung, Kanghoon Lee, Woohyung Lim. ICML 2025 Spotlight.

• Offline-to-Online Reinforcement Learning with Classifier-Free Diffusion Generation

  • Xiao Huang, Xu Liu, Enze Zhang, Tong Yu, Shuai Li. ICML 2025.

• Online Pre-Training for Offline-to-Online Reinforcement Learning

  • Yongjae Shin, Jeonghye Kim, Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Youngsoo Jang, Geonhyeong Kim, Jongseong Chae, Youngchul Sung, Kanghoon Lee, Woohyung Lim. ICML 2025.

• Leveraging Offline Data in Linear Latent Contextual Bandits

  • Chinmaya Kausik, Kevin Tan, Ambuj Tewari. ICML 2025. Bandit problem.

IJCAI 2025

• State Revisit and Re-explore: Bridging Sim-to-Real Gaps in Offline-and-Online Reinforcement Learning with An Imperfect Simulator
  • Xingyu Chen, Jiayi Xie, Zhijian Xu, Ruixun Liu, Shuai Yang, Zeyang Liu, Lipeng Wan, Xuguang Lan. IJCAI 2025.

AAAI 2025

• Offline-to-online hyperparameter transfer for stochastic bandits
  • Dravyansh Sharma, Arun Sai Suggala. AAAI 2025. Bandit problem.
• Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data
  • Shilong Deng, Zetao Zheng, Hongcai He, Paul Weng, Jie Shao. AAAI 2025.

ICLR 2025

• FOSP: Fine-tuning Offline Safe Policy through World Models

  • Chenyang Cao, Yucheng Xin, Silang Wu, Longxiang He, Zichen Yan, Junbo Tan, Xueqian Wang. ICLR 2025.

• Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data

  • Zhiyuan Zhou, Andy Peng, Qiyang Li, Sergey Levine, Aviral Kumar. ICLR 2025.

• Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model

  • Xiu Yuan, Tongzhou Mu, Stone Tao, Yunhao Fang, Mengke Zhang, Hao Su. ICLR 2025.

2024

ICRA 2024

• Weighting online decision transformer with episodic memory for offline-to-online reinforcement learning
  • Xiao Ma and Wu-Jun Li. ICRA 2024.

AAMAS 2024

• [ANOTO: Improving Automated Negotiation via Offline-to-Online Reinforcement Learning][https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p2195.pdf]
  • Siqi Chen, Jianing Zhao, Kai Zhao, Gerhard Weiss, Fengyun Zhang, Ran Su, Yang Dong, Daqian Li and Kaiyou Lei. AAMAS 2024 Extended Abstract.

NeurIPS 2024

• Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL
  • Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang, Sheng-Jun Huang. NeurIPS 2024.
• Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision Transformers
  • Kai Yan, Alexander G. Schwing, Yu-Xiong Wang. NeurIPS Spotlight 2024.
• Hybrid Reinforcement Learning Breaks Sample Size Barriers In Linear MDPs
  • Kevin Tan, Wei Fan, Yuting Wei. NeurIPS 2024.

ICML 2024

• Leveraging (Biased) Information: Multi-armed Bandits with Offline Data

  • Wang Chi Cheung, Lixing Lyu. ICML 2024 Spotlight. Bandit problem.

• Hybrid Reinforcement Learning from Offline Observation Alone

  • Yuda Song, J. Andrew Bagnell, Aarti Singh. ICML 2024. 06/2024.

• Bayesian Design Principles for Offline-to-Online Reinforcement Learning

  • Hao Hu, Yiqin Yang, Jianing Ye, Chengjie Wu, Ziqing Mai, Yujing Hu, Tangjie Lv, Changjie Fan, Qianchuan Zhao, Chongjie Zhang. ICML 2024.

• Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning

  • Xu-Hui Liu, Tian-Shuo Liu, Shengyi Jiang, Ruifeng Chen, Zhilong Zhang, Xinwei Chen, Yang Yu. ICML 2024.

• OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning

  • Sheng Yue, Xingyuan Hua, Ju Ren, Sen Lin, Junshan Zhang, Yaoxue Zhang. ICML 2024. Imitation Learning.

• Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL

  • Yu Luo, Tianying Ji, Fuchun Sun, Jianwei Zhang, Huazhe Xu, Xianyuan Zhan. ICML 2024.

• Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners

  • Chengjie Wu, Hao Hu, Yiqin Yang, Ning Zhang, Chongjie Zhang. ICML 2024.

AAAI 2024

• SUF: Stabilized Unconstrained Fine-Tuning for Offline-to-Online Reinforcement Learning

  • Jiaheng Feng, Mingxiao Feng, Haolin Song, Wengang Zhou, Houqiang Li. AAAI 2024.

• A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning

  • Yinmin Zhang, Jie Liu, Chuming Li, Yazhe Niu, Yaodong Yang, Yu Liu, Wanli Ouyang. AAAI 2024.

IJCAI 2024

• Efficient and Stable Offline-to-online Reinforcement Learning via Continual Policy Revitalization

  • Rui Kong, Chenyang Wu, Chen-Xiao Gao, Zongzhang Zhang, Ming Li. IJCAI 2024.

• ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles

  • Kai Zhao, Yi Ma, Jianye Hao, Jinyi Liu, Yan Zheng, Zhaopeng Meng. IJCAI 2024. arXiv. 06/2023.

RLC 2024

• Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning
  • Trevor McInroe, Stefano V. Albrecht, Amos Storkey. arXiv, reviewed at ICLR 2024.RLC 2024.

ICLR 2024

• Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees

  • Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun. ICLR 2024.

• Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization

  • Kun Lei, Zhengmao He, Chenhao Lu, Kaizhe Hu, Yang Gao, Huazhe Xu. ICLR 2024.

Underreviewed ICLR 24

• Adaptive Offline Data Replay in Offline-to-Online Reinforcement Learning

  • . reviewed at ICLR 2024.

• Bayesian Offline-to-Online Reinforcement Learning : A Realist Approach

  • Hao Hu, Yiqin Yang, Jianing Ye, Ziqing Mai, Yujing Hu, Tangjie Lv, Changjie Fan, Qianchuan Zhao, Chongjie Zhang. reviewed at ICLR 2024 -> Accepted at ICML 2024.

• SERA: Sample Efficient Reward Augmentation in offline-to-online Reinforcement Learning

  • Ziqi Zhang, Xiao Xiong, Zifeng Zhuang, Jinxin Liu, Donglin Wang. reviewed at ICLR 2024.

• Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning

  • Trevor McInroe, Stefano V. Albrecht, Amos Storkey. arXiv, reviewed at ICLR 2024. 10/2023.

• Offline RL for Online RL: Decoupled Policy Learning for Mitigating Exploration Bias

  • Max Sobol Mark, Archit Sharma, Fahim Tajwar, Rafael Rafailov, Sergey Levine, Chelsea Finn. arXiv, reviewed at ICLR 2024. 10/2023.

• A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning

  • Siyuan Guo, Yanchao Sun, Jifeng Hu, Sili Huang, Hechang Chen, Haiyin Piao, Lichao Sun, Yi Chang. reviewed at ICLR 2024.

• Automatic Fine-Tuned Offline-to-Online Reinforcement Learning via Increased Simple Moving Average Q-value

  • . reviewed at ICLR 2024.

• Guided Decoupled Exploration for Offline Reinforcement Learning Fine-tuning

  • . reviewed at ICLR 2024.

• Collaborative World Models: An Online-Offline Transfer RL Approach

  • Qi Wang, Junming Yang, Yunbo Wang, Xin Jin, Wenjun Zeng, Xiaokang Yang. reviewed at ICLR 2024.

2023

NeurIPS 2023

• Policy Finetuning in Reinforcement Learning via Design of Experiments using Offline Data
  • Ruiqi Zhang, Andrea Zanette. NeurIPS 2023.
• Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning
  • Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen. NeurIPS 2023.
• Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
  • Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine. NeurIPS 2023. 03/2023.

ICML 2023

• Jump-Start Reinforcement Learning

  • Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, Sergey Levine, Karol Hausman. ICML 2023. 04/22.

• Efficient Online Reinforcement Learning with Offline Data

  • Philip J. Ball, Laura Smith, Ilya Kostrikov, Sergey Levine. ICML 2023. 02/2023.

• Leveraging Offline Data in Online Reinforcement Learning

  • Andrew Wagenmaker, Aldo Pacchiano. ICML 2023. 11/2022.

• Actor-Critic Alignment for Offline-to-Online Reinforcement Learning

  • Zishun Yu, Xinhua Zhang. ICML 2023.

• Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap

  • Hang Wang, Sen Lin, Junshan Zhang. ICML 2023 Oral.

• Semi-Offline Reinforcement Learning for Optimized Text Generation

  • Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, Rui Yan. ICML 2023. Applied to Text Generation.

AAAI 2023

• Adaptive Policy Learning for Offline-to-Online Reinforcement Learning
  • Han Zheng, Xufang Luo, Pengfei Wei, Xuan Song, Dongsheng Li, Jing Jiang. AAAI 2023.

ICLR 2023

• Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
  • Haichao Zhang, We Xu, Haonan Yu. ICLR 2023. 02/2023.
• Hybrid RL: Using both offline and online data can make RL efficient
  • Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun. ICLR 2023

ICRA 2023

• Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision
  • Ashvin Nair, Brian Zhu, Gokul Narayanan, Eugen Solowjow, Sergey Levine. ICRA 2023.

CORL 2023

• MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning

  • Rafael Rafailov, Kyle Hatch, Victor Kolev, John D. Martin, Mariano Phielipp, Chelsea Finn. CORL 2023.

• Finetuning Offline World Models in the Real World

  • Yunhai Feng, Nicklas Hansen, Ziyan Xiong, Chandramouli Rajagopalan, Xiaolong Wang. CORL 2024 Oral. Model based finetuning.

2022

NeurIPS 2022

• Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress page
  • Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville, Marc G. Bellemare. NeurIPS 2022. 06/2022.

ICML 2022

• Online Decision Transformer

  • Qinqing Zheng, Amy Zhang, Aditya Grover. ICML 2022. 02/2022.

• Offline Meta-Reinforcement Learning with Online Self-Supervision

  • Vitchyr H. Pong, Ashvin Nair, Laura Smith, Catherine Huang, Sergey Levine. IML 2022. 07/2021.

ICLR 2022

• MOTO: Offline to Online Fine-tuning for Model-Based Reinforcement Learning
  • Rafael Rafailov, Kyle Beltran Hatch, Victor Kolev, John D Martin, Mariano Phielipp, Chelsea Finn. ICLR 2022 Workshop: Reincarnating Reinforcement Learning.

CORL 2022

• Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble

  • Seunghyun Lee, Younggyo Seo, Kimin Lee, Pieter Abbeel, Jinwoo Shin. CoRL 2022. 07/2021.

• Don’t Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning page

  • Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, Sergey Levine. CoRL 2022. 07/2022.

RSS 2022

• Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
  • Aviral Kumar, Anikait Singh, Frederik Ebert, Mitsuhiko Nakamoto, Yanlai Yang, Chelsea Finn, Sergey Levine. RSS. 10/2022.

Multi-Agent Reinforcement Learning

• Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning

  • Jiechuan Jiang, Zongqing Lu. AAAI 2023.

• M3: Modularization for Multi-task and Multi-agent Offline Pre-training

  • Linghui Meng, Jingqing Ruan, Xuantang Xiong, Xiyun Li, Xi Zhang, Dengpeng Xing, Bo Xu. AAMAS 2023.

• Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration

  • Hai Zhong, Xun Wang, Zhuoran Li, Longbo Huang. AAMAS 2025.

Journal Papers

• Offline-to-online reinforcement learning with policy ensemble and policy-extended value

  • Jiacheng Chen, Jin Zhu & Lin Yang . Applied Intelligence. 02 November 2025

• EMDSAC-ft: bridging the gap in offline-to-online reinforcement learning through value distribution learning

  • Yesen Chen, Teng Zhang, Tao Li, Dongyun Wang. Knowledge-Based Systems. 25 November 2025.

TMLR

• Towards Fast Safe Online Reinforcement Learning via Policy Finetuning
  • Keru Chen, Honghao Wei, Zhigang Deng, Sen Lin. TMLR Jan 2026.

⭐ Star History