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 14

Device-Cloud Collaborative LLM Inference with Multi-Modal, Multi-Task, Multi-Turn Conversations

arXiv:2502. 11007v5 Announce Type: replace Abstract: Compared to traditional machine learning models, recent large language models (LLMs) can exhibit multi-task-solving capabilities through multi-modal data sources and multi-turn conversations.

By Liangqi Yuan, Dong-Jun Han, Shiqiang Wang, Christopher G. Brinton

llms reinforcement-learning multimodal

arXiv AI

Jul 14

SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning

arXiv:2607. 10966v1 Announce Type: new Abstract: We introduce Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for multimodal reasoning.

By Mingyuan Wu, Jingcheng Yang, Shengyi Qian, Xudong Wang, Jize Jiang, Qifan Wang, Aashu Singh, Khoi Pham, Fei Liu, Zhaolun Su, Zhuokai Zhao, Klara Nahrstedt, Jianyu Wang, Hanchao Yu

reinforcement-learning multimodal benchmarks

arXiv AI

Jul 14

Think When It Matters: Conditional VLM Reasoning for Social Navigation with RL Policies

arXiv:2607. 10991v1 Announce Type: cross Abstract: As mobile robots become more integrated into everyday human environments, social robot navigation is becoming essential for ensuring human comfort, safety, and trust.

By Ali Ahmadi, Hamed Rahimi, Adrien Jacquet Cretides, Marie Samson, Mahdi Khoramshahi, Mohamed Chetouani

llms reinforcement-learning robotics multimodal benchmarks safety

arXiv AI

Jul 14

EvoCUA-1.5: Online Reinforcement Learning for Multi-turn Computer-Use Agents

arXiv:2607. 09773v1 Announce Type: new Abstract: Computer-use agents must solve long-horizon tasks through repeated interaction with partially observable, multimodal desktop environments.

By Mianqiu Huang, Taofeng Xue, Chong Peng, Jinrui Ding, Sicheng Fan, Jiale Hong, Yufei Gao, Xiaocheng Zhang, Linsen Guo, Xin Yang, Dengchang Zhao, Yuchen Xie, Peng Pei, Xunliang Xie, Xipeng Qiu

agents reinforcement-learning multimodal

arXiv AI

Jul 14

Learning from Local Walks on Dynamic Graphs with Bandit Feedback

arXiv:2607. 10571v1 Announce Type: cross Abstract: We study stochastic multi-armed bandits on dynamic graphs, where arms correspond to the vertices of a network with time-varying edges.

By Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni, Lijun Chen

reinforcement-learning safety

arXiv Machine Learning

Jul 14

First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints

arXiv:2603. 05774v2 Announce Type: replace Abstract: This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints.

By Zhankun Luo, Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi

reinforcement-learning

arXiv Machine Learning

Jul 14

Enhancing Reasoning for Diffusion LLMs via Distribution Matching Policy Optimization

arXiv:2510. 08233v3 Announce Type: replace Abstract: Diffusion large language models (dLLMs) are promising alternatives to autoregressive large language models (AR-LLMs), as they potentially allow higher inference throughput.

By Yuchen Zhu, Wei Guo, Jaemoo Choi, Petr Molodyk, Bo Yuan, Molei Tao, Yongxin Chen

llms diffusion reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 14

Depth-Entropy Guided Sampling for Training-Free LLM Reasoning

arXiv:2607. 09693v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become the dominant paradigm for improving the reasoning capabilities of large language models, but it requires expensive training, curated data, and reward signals.

By Zibin Meng, Peng Xie, Kani Chen

llms reinforcement-learning benchmarks

arXiv AI

Jul 14

Constrained Reinforcement Learning for Safe Heat Pump Control

arXiv:2409. 19716v2 Announce Type: replace-cross Abstract: Constrained Reinforcement Learning (RL) has emerged as a significant research area within RL, where integrating constraints with rewards is crucial for enhancing safety and performance across diverse control tasks.

By Baohe Zhang, Lilli Frison, Thomas Brox, Joschka B\"odecker

reinforcement-learning benchmarks safety

arXiv AI

Jul 14

Adaptive Reinforcement Learning for Unobservable Random Delays

arXiv:2506. 14411v2 Announce Type: replace-cross Abstract: In standard reinforcement learning (RL) settings, the interaction between the agent and the environment is typically modeled as a Markov decision process (MDP), which assumes that the agent observes the system state instantaneously, selects an action without delay, and executes it immediately.

By John Wikman, Alexandre Proutiere, David Broman

agents reinforcement-learning robotics benchmarks

arXiv AI

Jul 14

SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

arXiv:2607. 11185v1 Announce Type: new Abstract: Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution.

By Bowen Lv, Xiao Liu, Yanyu Ren, Hanyu Lai, Bohao Jing, Hanchen Zhang, Yanxiao Zhao, Shuntian Yao, Jie Tang, Yuxiao Dong

agents computer-vision reinforcement-learning benchmarks

arXiv AI

Jul 14

RVN-Bench: A Benchmark for Reactive Visual Navigation

arXiv:2603. 03953v2 Announce Type: replace-cross Abstract: Safe visual navigation is critical for indoor mobile robots operating in cluttered environments.

By Jaewon Lee, Jaeseok Heo, Gunmin Lee, Howoong Jun, Jeongwoo Oh, Songhwai Oh

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jul 14

Beyond Slater's Condition in Online CMDPs with Stochastic and Adversarial Constraints

arXiv:2509. 20114v3 Announce Type: replace Abstract: We study \emph{online episodic Constrained Markov Decision Processes} (CMDPs) under both stochastic and adversarial constraints.

By Francesco Emanuele Stradi, Eleonora Fidelia Chiefari, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti

reinforcement-learning benchmarks safety

arXiv AI

Jul 14

BRIDGE: Bridging Reasoning In Distillation Gap Elimination via Structure-Aware Masking

arXiv:2602. 17686v5 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) reasoning has significantly improved LLMs' mathematical problem-solving capabilities, but distilling such capabilities into smaller models remains challenging due to the capacity mismatch between verbose teachers and compact students.

By Bowen Yu, Sheng Zhang, Binhao Wang, Yi Wen, Jingtong Gao, Bowen Liu, Zimo Zhao, Shanshan Ye, Wanyu Wang, Maolin Wang, Xiangyu Zhao

llms reinforcement-learning efficiency safety

arXiv AI

Jul 14

DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs

arXiv:2607. 11228v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases.

By Anqi Li, Jie Zhang, Zhongqi Wang, Songkai Xue, Jiahao Wang, Shiguang Shan, Xilin Chen

llms agents reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Jul 14

SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning

arXiv:2607. 11624v1 Announce Type: cross Abstract: Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency.

By Evelyn D'Elia, Weishu Zhan, Giulio Turrisi, Giulio Romualdi, Giuseppe L'Erario, Raffaello Camoriano, Wei Pan, Daniele Pucci

agents reinforcement-learning robotics benchmarks

arXiv AI

Jul 14

ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples

arXiv:2607. 10481v1 Announce Type: cross Abstract: Reinforcement learning (RL) has significantly enhanced the reasoning capabilities of large language models (LLMs), yet the training process remains notoriously fragile.

By Kexin Huang, Junkang Wu, Jinda Lu, Shuo Yang, Chiyu Ma, Jiancan Wu, Xiang Wang, Xiangnan He, Guoyin Wang, Jingren Zhou

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jul 14

A Risk-Field Enhanced Closed-Loop Digital Twin Framework for Autonomous Driving Safety Validation

arXiv:2607. 09772v1 Announce Type: cross Abstract: Autonomous driving systems require reliable safety validation before real-world deployment.

By Yongzhi Liu

rag agents reinforcement-learning robotics safety

arXiv AI

Jul 14

Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs

arXiv:2410. 03565v4 Announce Type: replace-cross Abstract: In the zero-shot policy transfer (ZSPT) setting for contextual Markov decision processes (CMDP), agents train on a fixed, finite set of contexts and must generalize to new ones.

By Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan, Wendelin B\"ohmer

agents reinforcement-learning benchmarks

arXiv AI

Jul 14

A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation

arXiv:2607. 11874v1 Announce Type: cross Abstract: Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them.

By Yunhai Feng, Natalie Leung, Jiaxuan Wang, Lujie Yang, Haozhi Qi, Preston Culbertson

reinforcement-learning robotics

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