Reinforcement learning

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

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arXiv Machine Learning

Jun 17

Deep Reinforcement Learning for Minimum Zero-Forcing Sets

arXiv:2606. 18106v1 Announce Type: new Abstract: This paper explores the problem of finding the minimum zero-forcing set on undirected graphs and proposes an adapted machine-learning framework to solve the problem.

By Steve Halley, Maur\'icio Gruppi

reinforcement-learning

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 AI

Jun 17

TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations

arXiv:2606. 17386v1 Announce Type: cross Abstract: End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments.

By Zikang Xiong, Weixin Li, Zhouchonghao Wu, Akshay Rangesh, Saarth Bonde, Grantland Hall, Chen Tang, Yihan Hu, Wei Zhan

agents diffusion reinforcement-learning robotics benchmarks safety

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

Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation

arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.

By Xinyu Qin, Anil K. Sood, Ruiheng Yu, Sara Corvigno, Elaine Stur, Lu Wang

reinforcement-learning safety

arXiv AI

Jun 17

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

arXiv:2606. 18111v1 Announce Type: cross Abstract: Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives.

By Umer Siddique, Peilang Li, Yongcan Cao

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 17

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

arXiv:2602. 11715v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) have emerged as a compelling alternative to autoregressive (AR) LLMs, owing to their capacity for parallel token generation.

By Haolei Bai, Lingcheng Kong, Xueyi Chen, Jianmian Wang, Zhiqiang Tao, Huan Wang

llms diffusion reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 17

Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents

arXiv:2606. 18223v1 Announce Type: cross Abstract: With sophisticated cyber-attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber-defense agents trained via Reinforcement Learning (RL).

By Ankita Samaddar, Sandeep Neema, Daniel Balasubramanian, Xenofon Koutsoukos

agents reinforcement-learning

arXiv Machine Learning

Jun 17

Learning Upper Lower Value Envelopes to Shape Online RL: A Principled Approach

arXiv:2510. 19528v2 Announce Type: replace-cross Abstract: We investigate the fundamental problem of leveraging offline data to accelerate online reinforcement learning - a direction with strong potential but limited theoretical grounding.

By Sebastian Reboul, H\'el\`ene Halconruy

reinforcement-learning fine-tuning

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

OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction

arXiv:2509. 26633v3 Announce Type: replace-cross Abstract: A dominant paradigm for teaching humanoid robots complex skills is to retarget human motions as kinematic references to train reinforcement learning (RL) policies.

By Lujie Yang, Xiaoyu Huang, Zhen Wu, Angjoo Kanazawa, Pieter Abbeel, Carmelo Sferrazza, C. Karen Liu, Rocky Duan, Guanya Shi

agents reinforcement-learning robotics

arXiv AI

Jun 17

StepGuard: Guarding Web Navigation via Single-Step Calibration

arXiv:2606. 17871v1 Announce Type: new Abstract: Web navigation requires agents to follow natural language goals, interact with web pages, and produce accurate answers.

By Zhihao Cui, Yuchen Zhang, Xiyang Sun, Yaxiong Wang, Li Zhu, Jinpeng Hu, Liu Liu, Mengjia Li, Yujiao Wu

llms agents reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jun 17

Learning in Matching Games with Bandit Feedback

arXiv:2506. 03802v2 Announce Type: replace Abstract: We introduce a learning problem in a generalized two-sided matching market, where agents select actions to interact with their match.

By Andreas Athanasopoulos, Christos Dimitrakakis

agents reinforcement-learning

arXiv AI

Jun 17

Planning with the Views

arXiv:2605. 29563v3 Announce Type: replace Abstract: Can VLMs predict how each camera move changes the view, and plan many such moves ahead?

By Kangrui Wang, Linjie Li, Zhengyuan Yang, Shiqi Chen, Zihan Wang, Li Fei-Fei, Jiajun Wu, Leonidas Guibas, Lijuan Wang, Manling Li

llms reinforcement-learning efficiency

arXiv AI

Jun 17

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

arXiv:2602. 10635v3 Announce Type: replace Abstract: Socially intelligent AI systems must reason across diverse human behavioral tasks and generalize to new social contexts.

By Keane Ong, Sabri Boughorbel, Luwei Xiao, Chanakya Ekbote, Wei Dai, Ao Qu, Jingyao Wu, Rui Mao, Ehsan Hoque, Erik Cambria, Gianmarco Mengaldo, Paul Pu Liang

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 17

Continuous-time Optimal Stopping through Deep Reinforcement Learning

arXiv:2606. 17545v1 Announce Type: new Abstract: Simulation based solvers for optimal stopping problems must discretize the stopping decision.

By Cosmin Borsa, Michael Ludkovski

reinforcement-learning

arXiv AI

Jun 17

Online LLM Selection via Constrained Bandits with Time-Varying Demand

arXiv:2606. 17489v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles.

By Yin Huang, Qingsong Liu, Jie Xu

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 17

Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferences

arXiv:2602. 23116v3 Announce Type: replace Abstract: We consider the problem of regularized best-response max-regret minimization in online RLHF under general preferences and bandit feedback.

By Junghyun Lee, Minju Hong, Kwang-Sung Jun, Chulhee Yun, Se-Young Yun

reinforcement-learning safety

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

arXiv Machine Learning

Jun 17

Continual Self-Improvement with Lightweight Experiential Latent Memories

arXiv:2606. 17803v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning traces generated during this process.

By Vaggelis Dorovatas, Nancy Kalaj, Rahaf Aljundi

llms reinforcement-learning benchmarks

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