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

Jul 7

Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX

arXiv:2510. 01764v3 Announce Type: replace Abstract: Reinforcement learning (RL) research requires diverse, challenging environments that are both tractable and scalable.

By Waris Radji, Thomas Michel, Hector Piteau

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 7

Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading

arXiv:2607. 02864v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction.

By Lin Li, Li Rong Wang, Hsuan Fu, Xiuyi Fan

agents reinforcement-learning

arXiv AI

Jul 7

Explainable Reinforcement Learning for Adaptive Traffic Signal Control

arXiv:2607. 03703v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control.

By Dickens Kwesiga, Nishu Choudhary, Angshuman Guin, Michael Hunter

reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 7

Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming

arXiv:2607. 04096v1 Announce Type: new Abstract: Current agentic workflows usually involve decomposing user requests into sequences of tool calls with correctly resolved parameters, the results of which are processed through reasoning traces in the language model's context window.

By Vishvesh Bhat, Jay Vaghasiya, Emmanuel Anaya Gonzalez

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 7

MentalThink: Shaping Thoughts in Mental SVG World

arXiv:2607. 03530v1 Announce Type: new Abstract: We introduce MentalThink, a visual-symbolic reasoning paradigm that equips Multimodal LLMs (MLLMs) with an executable mechanism for "mental" visualization.

By Kangheng Lin, Jisheng Yin, Dingming Li, En Yu, Yana Wei, Han Zhou, Liang Zhao, Hongyu Zhou, Hongbo Peng, Jianjian Sun, Zheng Ge, Xiangyu Zhang, Daxin Jiang, Jingyu Wang

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 7

TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance

arXiv:2510. 08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, where semantic understanding and complex reasoning directly shape the user experience and business conversion.

By Jianhui Yang, Yiming Jin, Pengkun Jiao, Chenhe Dong, Zerui Huang, Shaowei Yao, Xiaojiang Zhou, Dan Ou, Haihong Tang

llms reinforcement-learning fine-tuning

arXiv AI

Jul 7

ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

arXiv:2602. 21534v3 Announce Type: replace Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks.

By Xiaoxuan Wang, Han Zhang, Haixin Wang, Yidan Shi, Ruoyan Li, Kaiqiao Han, Chenyi Tong, Haoran Deng, Renliang Sun, Alexander Taylor, Yanqiao Zhu, Jason Cong, Yizhou Sun, Wei Wang

llms agents reinforcement-learning

arXiv Machine Learning

Jul 7

Understanding electricity consumption behaviour through Inverse Reinforcement Learning

arXiv:2607. 03176v1 Announce Type: new Abstract: Understanding how households consume electricity in response to socioeconomic and climatic drivers is important for decision-makers designing energy policies in a changing climate and under geopolitical tensions.

By Enrico Cofler, Carlos Rodriguez-Pardo, Matteo Giuliani, Andrea Castelletti, Massimo Tavoni

agents reinforcement-learning

arXiv Machine Learning

Jul 7

Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System

arXiv:2607. 03125v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its "black-box" exploration risks violating strict hardware safety limits.

By Georg Sch\"afer, Jakob Rehrl, Stefan Huber

reinforcement-learning safety

arXiv AI

Jul 7

ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy

arXiv:2607. 03126v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make token-level credit assignment difficult.

By Zijun Xie, Yuyang You, Yongzhi Li, Enlei Gong, Zeyu Chen, Quan Chen, Yanhua Cheng, Peng Jiang, Yadong Mu

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 7

Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions

arXiv:2602. 07341v2 Announce Type: replace Abstract: This paper focuses on the scalable robot learning for manipulation in the dexterous robot arm-hand systems, where the remote human-robot interactions via augmented reality (AR) are established to collect the expert demonstration data for improving efficiency.

By Yicheng Yang, Ruijiao Li, Lifeng Wang, Shuai Zheng, Shunzheng Ma, Keyu Zhang, Tuoyu Sun, Chenyun Dai, Jie Ding, Zhuo Zou

reinforcement-learning robotics benchmarks safety

arXiv AI

Jul 7

LLM-as-a-Verifier: A General-Purpose Verification Framework

arXiv:2607. 05391v1 Announce Type: new Abstract: Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs.

By Jacky Kwok, Shulu Li, Pranav Atreya, Yuejiang Liu, Yixing Jiang, Chelsea Finn, Marco Pavone, Ion Stoica, Azalia Mirhoseini

llms agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jul 7

On the effectiveness of reward functions in reinforcement learning for confidence calibration of large language models

arXiv:2607. 04332v1 Announce Type: new Abstract: In this paper, we consider the setting where large language models (LLMs) are trained using reinforcement learning (RL) to simultaneously improve reasoning accuracy and verbalize its confidence.

By Chee Heng Tan, Zhuoyi Lin, Mehul Motani, Wee Sun Lee

llms reinforcement-learning

arXiv AI

Jul 7

Out-of-Distribution Generalization of Risk Aversion in Language Models

arXiv:2607. 02755v1 Announce Type: cross Abstract: Training AIs to be risk-averse in resources could offer a failsafe in the event that AIs turn out misaligned.

By Kristina Zhang, Junior Chinomso Okoroafor, Benjamin Maltbie, Andrew Lin, Abhitej Bokka, Elliott Thornley

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 7

Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding

arXiv:2607. 04383v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Detection attains frame-level precision only over a closed label set.

By Zihan Zhang, Xize Cheng, Wenhao Yan, Tong Zhang, Dongjie Fu, Boyun Zhang, Yongbo He, Tao Jin

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 7

Unbiased Alignment for Large Language Models with Noisy Preferences

arXiv:2607. 03248v1 Announce Type: cross Abstract: The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Preference Optimization.

By Jialiang Wang, Xianming Liu, Xiong Zhou, Hui Liu, Haoliang Li

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 7

Training Verifiably Robust Agents Using Set-Based Reinforcement Learning

arXiv:2408. 09112v2 Announce Type: replace Abstract: Reinforcement learning policies parametrized by deep neural networks have achieved strong performance for continuous control, yet even small input perturbations may lead to unpredictable behavior.

By Manuel Wendl, Lukas Koller, Tobias Ladner, Matthias Althoff

agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 7

Dynamic Regret for Non-Stationary Linear Bandits via Misspecification Reductions

arXiv:2607. 02891v1 Announce Type: new Abstract: Many online decision-making problems involve both round-specific feasible actions and drifting reward models: eligible ad impressions, feasible prices, and available treatments can change over time, while user preferences, demand curves, and patient responses may evolve.

By Zihao Hu, Yuan Yao, Jiheng Zhang, Zhengyuan Zhou

reinforcement-learning benchmarks

arXiv AI

Jul 7

Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models

arXiv:2607. 02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that assumes complete information.

By Shengyi Hua, Kangzhe Hu, Conghui He, Xiaofan Zhang, Shaoting Zhang

llms rag agents reinforcement-learning

arXiv AI

Jul 7

CONFLUX: A Latent Diusion Model for 3D Chest-CT Synthesis with RL Post-Training

arXiv:2607. 02998v1 Announce Type: cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.

By Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms diffusion reinforcement-learning

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