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 4

Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text

arXiv:2605. 29076v2 Announce Type: replace-cross Abstract: LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited reasoning on complex text and lacks broader model transparency, while discrete prompt optimization offers human-readable instructions but struggles with performance and scalability.

By Tianyang Zhou, Wenbo Chen, Pierre Jinghong Liang, Leman Akoglu

llms nlp reinforcement-learning fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Jun 4

Policy Improvement Reinforcement Learning

arXiv:2604. 00860v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a central post-training paradigm for improving the reasoning capabilities of large language models.

By Huaiyang Wang, Xiaojie Li, Deqing Wang, Haoyi Zhou, Zixuan Huang, Yaodong Yang, Jianxin Li, Yikun Ban

llms reinforcement-learning benchmarks

arXiv AI

Jun 4

CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation

arXiv:2606. 04718v1 Announce Type: cross Abstract: Humans primarily rely on walking and running to traverse complex terrains, without resorting to unnecessarily complex motion patterns.

By Kailun Huang (Hong Kong University of Science and Technology), Zikang Xie (Hong Kong University of Science and Technology), Yanzhe Xie (Hong Kong University of Science and Technology), Panpan Liao (Guangdong University of Technology), Fanghai Zhang (Hong Kong University of Science and Technology), Yanheng Mai (Hong Kong University of Science and Technology), Wenhao Xu (South China Agricultural University), Yunheng Wang (Hong Kong University of Science and Technology), Renjing Xu (Hong Kong University of Science and Technology), Haohui Huang (Guangdong University of Technology)

reinforcement-learning robotics

arXiv Machine Learning

Jun 4

ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization

arXiv:2603. 21180v4 Announce Type: replace Abstract: Sequential experimental design under expensive, gradient-free objectives is a central challenge in computational statistics: evaluation budgets are tightly constrained and information must be extracted efficiently from each observation.

By Foo Hui-Mean, Yuan-chin I Chang

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 4

Policy Gradient for Continuous-Time Robust Markov Decision Processes

arXiv:2606. 04335v1 Announce Type: new Abstract: The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst-case transition dynamics.

By Tanya Veeravalli, David M. Bossens, Atsushi Nitanda

agents reinforcement-learning safety

arXiv Machine Learning

Jun 4

Self-Distilled Policy Gradient

arXiv:2606. 04036v1 Announce Type: new Abstract: On-policy self-distillation, where a language model conditions on privileged context to supervise its own generations, is a promising source of dense supervision for sparse-reward reinforcement learning.

By Yifeng Liu, Shiyuan Zhang, Yifan Zhang, Quanquan Gu

llms reinforcement-learning efficiency

arXiv AI

Jun 4

SUSD: Structured Unsupervised Skill Discovery through State Factorization

arXiv:2602. 01619v2 Announce Type: replace-cross Abstract: Unsupervised Skill Discovery (USD) aims to autonomously learn a diverse set of skills without relying on extrinsic rewards.

By Seyed Mohammad Hadi Hosseini, Mahdieh Soleymani Baghshah

agents reinforcement-learning

arXiv AI

Jun 4

Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning

arXiv:2606. 04923v1 Announce Type: cross Abstract: Rubric-based reinforcement learning (RL) uses an LLM-as-a-Judge (LaaJ) to score model outputs according to rubrics as rewards.

By Xuekang Wang, Zhuoyuan Hao, Shuo Hou, Hao Peng, Juanzi Li, Xiaozhi Wang

llms agents reinforcement-learning

arXiv AI

Jun 4

EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms

arXiv:2606. 04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality.

By Guilin Zhang, Chuanyi Sun, Shahryar Sarkani, John M. Fossaceca

llms reinforcement-learning fine-tuning

arXiv AI

Jun 4

Reinforcement Learning from Rich Feedback with Distributional DAgger

arXiv:2606. 05152v1 Announce Type: cross Abstract: Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward each with a single bit indicating whether the final answer is correct.

By Rishabh Agrawal, Jacob Fein-Ashley, Paria Rashidinejad

reinforcement-learning efficiency

arXiv AI

Jun 4

SaliMory: Orchestrating Cognitive Memory for Conversational Agents

arXiv:2606. 04120v1 Announce Type: cross Abstract: Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions.

By Kai Zhang, Xinyuan Zhang, Hongda Jiang, Shiun-Zu Kuo, Hyokun Yun, Ejaz Ahmed, Shereen Oraby, Ziyun Li, Sanat Sharma, Ann Lee, Ahmed A Aly, Anuj Kumar, Raffay Hamid, Xin Luna Dong

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 4

Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring

arXiv:2604. 12645v2 Announce Type: replace-cross Abstract: Although autonomous underwater vehicles promise the capability of marine ecosystem monitoring, their deployment is fundamentally limited by the difficulty of controlling vehicles under highly uncertain and non-stationary underwater dynamics.

By Melvin Laux, Yi-Ling Liu, Rina Alo, S\"oren T\"opper, Mariela De Lucas Alvarez, Frank Kirchner, Rebecca Adam

agents reinforcement-learning

arXiv Machine Learning

Jun 4

Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning

arXiv:2601. 07408v2 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) has emerged as a promising critic-free reinforcement learning paradigm for reasoning tasks.

By Ziheng Li, Liu Kang, Feng Xiao, Luxi Xing, Qingyi Si, Zhuoran Li, Weikang Gong, Deqing Yang, Yanghua Xiao, Hongcheng Guo

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 4

Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments

arXiv:2209. 15448v3 Announce Type: replace Abstract: As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority.

By Jiayi Wang, Zhengling Qi, Chengchun Shi

agents reinforcement-learning

arXiv Machine Learning

Jun 4

Bayesian learning for the stochastic shortest path problem

arXiv:2606. 04845v1 Announce Type: cross Abstract: Sequential decision-making problems are often modelled as a Markov decision process (MDP).

By Chon Wai Ho, Sumeetpal S. Singh, Jiaqi Guo

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 4

MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU

arXiv:2606. 04847v1 Announce Type: cross Abstract: Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code.

By Kun Cheng, Songshuo Lu, Sicong Liao, Tankun Li, Yafei Zhang, Dong Yang, Qiheng Lv, Hua Wang, Zhi Chen, Yaohua Tang

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 4

VentAgent: When LLMs Learn to Breathe -- Multi-Objective Arbitration for ARDS Ventilation

arXiv:2606. 04632v1 Announce Type: new Abstract: Mechanical ventilation for Acute Respiratory Distress Syndrome (ARDS) requires balancing competing physiological goals, including oxygenation, lung protection, and acid-base homeostasis.

By Teqi Hao, Yuxuan Fu, Xiaoyu Tan, Shaojie Shi, Bohao Lv, Yinghui Xu, Xihe Qiu

llms agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 4

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

arXiv:2606. 04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets.

By Damian Lebied\'z, Robert \'Slepaczuk

agents reinforcement-learning

arXiv Machine Learning

Jun 4

Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning

arXiv:2606. 04492v1 Announce Type: new Abstract: Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks.

By Zicheng Zhao, Yu Lan, Chengzhengxu Li, Zhaohan Zhang, Xiaoming Liu

agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jun 4

Offline-to-Online Learning in Linear Bandits

arXiv:2606. 04305v1 Announce Type: new Abstract: We study online learning with an additional offline dataset in the stochastic linear bandit setting.

By Kushagra Chandak, Toshinori Kitamura, Xiaoqi Tan

reinforcement-learning

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