Reinforcement learning

Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.

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arXiv AI

Jun 17

Reversal Q-Learning

arXiv:2606. 17551v1 Announce Type: cross Abstract: Iterative generative modeling techniques, such as flow matching, provide powerful tools to model complex behaviors for effective offline reinforcement learning (RL).

By Aditya Oberai, Seohong Park, Sergey Levine

diffusion reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jun 17

Reward hacking in physical reinforcement learning revealed by turbulent drag reduction

arXiv:2606. 06227v2 Announce Type: replace-cross Abstract: A reinforcement-learning agent maximises its reward, which can diverge from the outcome its designer intended.

By Giorgio Maria Cavallazzi, Miguel P\'erez-Cuadrado, Alfredo Pinelli

agents reinforcement-learning

arXiv AI

Jun 17

When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning

arXiv:2605. 05172v2 Announce Type: replace-cross Abstract: Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning.

By Lakshita Dodeja, Ondrej Biza, Shivam Vats, Stephen Hart, Stefanie Tellex, Robin Walters, Karl Schmeckpeper, Thomas Weng

reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jun 17

Theoretical Grounding of Out-Of-Distribution Detection With Reinforcement Learning Optimizer

arXiv:2606. 17477v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection in dynamic open-world environments requires a model to continually adapt to evolving data distributions while generalizing to covariate-shifted inputs and rejecting semantic-shifted OOD examples.

By Salimeh Sekeh, Xin Zhang

reinforcement-learning

arXiv Machine Learning

Jun 17

EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning

arXiv:2606. 17680v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for training Large Language Models (LLMs) as agents.

By Zhitong Wang, Songze Li, Hao Peng, Shuzheng Si, Yi Wang, Maosong Sun, Juanzi Li

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 17

Decision-Driven Geosteering Under Uncertainty: A Unified Framework for Sequential Decision Optimization

arXiv:2606. 17331v1 Announce Type: new Abstract: Geosteering requires navigating a well trajectory through an unknown geological configuration, while sequentially updating decisions based on indirect measurements acquired during drilling.

By Hibat Errahmen Djecta, Sergey Alyaev, Kristian Fossum, Reidar B. Bratvold, Ressi Bonti Muhammad, Apoorv Srivastava

reinforcement-learning

arXiv AI

Jun 17

MagicSim: A Unified Infrastructure for Executable Embodied Interaction

arXiv:2606. 17511v1 Announce Type: cross Abstract: Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment.

By Haoran Lu, Songling Liu, Yue Chen, Guo Ye, Mutian Shen, Shuyang Yu, Yu Xiao, Jihai Zhao, Shang Wu, Jianshu Zhang, Xiangtian Gui, Chuye Hong, Yuran Wang, Maojiang Su, Jiayi Wang, Ruihai Wu, Zhaoran Wang, Han Liu

agents reinforcement-learning robotics multimodal benchmarks

arXiv Machine Learning

Jun 17

OmniPlan: An Adaptive Framework for Timely and Near-Optimal Network Planning Optimization

arXiv:2606. 18105v1 Announce Type: cross Abstract: Network planning optimization is a fundamental problem across diverse domains, including transportation systems, communication networks, and power grids.

By Longlong Zhu, Jiashuo Yu, Zedi Chen, Yuhan Wu, Zhifan Jiang, Yuchen Xian, Yimeng Liu, Jiajie Su, Shaopeng Zhou, Xingyuan Li, Hongyan Liu, Xuan Liu, Dong Zhang, Chunming Wu, Xiang Chen

llms reinforcement-learning

arXiv AI

Jun 17

SP-GCRL: Influence Maximization on Incomplete Social Graphs

arXiv:2605. 12513v2 Announce Type: replace-cross Abstract: Influence maximization (IM) in real platforms is challenged by incomplete, noisy social graphs and non-stationary diffusion dynamics.

By Haohua Niu, Yuxuan Yang, Lingfeng Zhang, Hao Li, Jiao Liang, Zongfu Luo, Luca Rossi

diffusion reinforcement-learning

arXiv AI

Jun 17

m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning

arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.

By Yosub Shin, Michael Buriek, Igor Molybog

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 17

Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks

arXiv:2510. 21127v2 Announce Type: replace-cross Abstract: Despite rapid advancements in sensor networks, conventional battery-powered sensor networks suffer from limited operational lifespans and frequent maintenance requirements that severely constrain their deployment in remote and inaccessible environments.

By Bowei Tong, Hui Kang, Jiahui Li, Geng Sun, Jiacheng Wang, Yaoqi Yang, Bo Xu, Dusit Niyato

reinforcement-learning

arXiv AI

Jun 17

Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies

arXiv:2502. 17518v3 Announce Type: replace-cross Abstract: This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance.

By Zheli Xiong

agents reinforcement-learning robotics

arXiv AI

Jun 17

PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space

arXiv:2606. 17924v1 Announce Type: cross Abstract: Current Vision-Language-Action (VLA) models face a trade-off between efficient action generation and explicit deliberation.

By Bochen Yang, Lianlei Shan

llms diffusion reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jun 17

Knowledge Reutilization in Meta-Reinforcement Learning

arXiv:2606. 18132v1 Announce Type: new Abstract: Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-specific control.

By Yuan Meng, Bo Wang, Juan de los Rios Ruiz, Xiangtong Yao, Zhenshan Bing, Fuchun Sun, Alois Knoll

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jun 17

From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoning

arXiv:2606. 18089v1 Announce Type: new Abstract: Post-training pipelines that combine supervised fine-tuning (SFT) with reinforcement learning (RL) have emerged as the key recipe for transforming large language models (LLMs) into robust reasoners.

By Lingjing Kong, Xin Liu, Guangyi Chen, Martin Q. Ma, Xiangchen Song, Yuekai Sun, Mikhail Yurochkin, Taylor W. Killian, Ruslan Salakhutdinov, Kun Zhang, Eric P. Xing, Zhengzhong Liu

llms reinforcement-learning fine-tuning

arXiv AI

Jun 17

Principled RL for Flow Matching Emerges from the Chunk-level Policy Optimization

arXiv:2510. 21583v3 Announce Type: replace-cross Abstract: Recent Progress in post-training flow matching for text-to-image (T2I) generation with Group Relative Policy Optimization (GRPO) has demonstrated strong potential.

By Yifu Luo, Haoyuan Sun, Xinhao Hu, Penghui Du, Keyu Fan, Bo Li, Sinan Du, Xu Wan, Zhiyu Chen, Bo Xia, Yongzhe Chang, Changqian Yu, Kun Gai, Tiantian Zhang, Xueqian Wang

rag diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 17

Learning to Refine Hidden States for Reliable LLM Reasoning

arXiv:2606. 17524v1 Announce Type: new Abstract: Large language models show strong reasoning ability, but their internal reasoning process can remain unstable in complex multi-step settings, where early hidden-state errors may propagate to incorrect predictions.

By Chia-Hsuan Hsu, Jui-Ming Yao

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 17

Rethinking Groups in Critic-Free RLVR

arXiv:2606. 17250v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a central paradigm for post-training large language models.

By Yihong Wu, Liheng Ma, Lingfeng Xiao, Muzhi Li, Xinyu Wang, Yingxue Zhang, Jian-Yun Nie

llms agents reinforcement-learning

arXiv Machine Learning

Jun 17

Performance-Driven Environment Abstraction with Multi-Timescale Learning

arXiv:2606. 17377v1 Announce Type: new Abstract: We study performance-driven environment abstraction for decision-making in large Markov decision processes.

By Yue Guan, Dipankar Maity, Panagiotis Tsiotras

reinforcement-learning

arXiv AI

Jun 17

Enhancing Pathological VLMs with Cross-scale Reasoning

arXiv:2606. 17412v1 Announce Type: cross Abstract: Pathological images are inherently multi-scale, requiring pathologists to integrate evidence from global tissue architecture at low magnification to cellular morphology at higher magnification for accurate diagnosis.

By Chi Phan, Tianyi Zhang, Qiaochu Xue, Yufeng Wu, Dan Hu, Zeyu Liu, Sudong Wang, Yueming Jin

llms nlp reinforcement-learning multimodal benchmarks safety

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