Reinforcement learning

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

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arXiv AI

Aug 12

Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education

arXiv:2605. 15850v3 Announce Type: replace-cross Abstract: In recent years, generative AI (GenAI) in educational settings has become ubiquitous in university students' daily lives, despite its potential to induce over-reliance, metacognitive disengagement, and diminished learning when used unrestrictedly.

By Janne Rotter, Pau Benazet i Montobbio, Davinia Hern\'andez-Leo

agents reinforcement-learning

arXiv AI

Aug 12

Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

arXiv:2608. 10954v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes under adverse conditions.

By Zhaoyang Wei, Bowen Jiang, Xumeng Han, Jiashu Li, Xuehui Yu, Yuling Liu, Guorong Li, Zhenjun Han, Jianbin Jiao

llms reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Aug 12

Lipschitz Dueling Bandits over Continuous Action Spaces

arXiv:2604. 00523v2 Announce Type: replace Abstract: We study for the first time, stochastic dueling bandits over continuous action spaces with Lipschitz structure, where feedback is purely comparative.

By Mudit Sharma, Shweta Jain, Vaneet Aggarwal, Ganesh Ghalme

reinforcement-learning

arXiv Machine Learning

Aug 12

Scaling Self-Play with Self-Guidance

arXiv:2604. 20209v2 Announce Type: replace Abstract: LLM self-play algorithms are notable in that, in principle, nothing bounds their learning: a Conjecturer model creates problems for a Solver, and both improve together.

By Luke Bailey, Kaiyue Wen, Kefan Dong, Tatsunori Hashimoto, Tengyu Ma

llms reinforcement-learning

arXiv Machine Learning

Aug 12

Scheduling Mixed RL Rollouts Beyond Prefix Locality

arXiv:2608. 11152v1 Announce Type: cross Abstract: Modern reinforcement learning (RL) post-training pipelines for large language models (LLMs) increasingly combine rollout workloads across multiple domains and feedback paradigms.

By Zetao Hong, Song Yuan, Yuanhao Ding, Yibo Zhu, Daxin Jiang, Zhibin Wang, Chen Tian

llms agents reinforcement-learning efficiency

arXiv Machine Learning

Aug 12

Topological Feasibility Guarantees for Differentiable Predictive Control

arXiv:2608. 10332v1 Announce Type: cross Abstract: Differentiable predictive control (DPC), a self-supervised learning approach for approximating explicit model predictive control (MPC) policies, offers significant computational advantages over online optimization-based MPC.

By Guangyu Wu, J\'an Drgo\v{n}a

reinforcement-learning safety

arXiv Machine Learning

Aug 12

MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction

arXiv:2608. 10562v1 Announce Type: new Abstract: Not all clicks are equal.

By Shiwen Shen, Xiru Huang, Liang Luo, Jianbo Sun, He Lyu, Zihang Fu, Ivonne Xu, Zhizhuo Li, Zhengyu Zhang, Pei-Ju Sung, Yunmiao Wang, Zixuan Wang, Zhengli Zhao, Qiang Jin, Mike Jermann, Mingda Li, Yang Xiao, Bhavana Challa, Brooke Bian, Yang Li, Ashish Chamoli, Bibek Bhusal, Danning Di, Yuan Jin, Meet Raval, Zhiwen Chen, Boyao Sun, Shuguang Wang, Yunlong He, Yantao Yao, Sagar Chordia, Wenlin Chen, Santanu Kolay, Qin Huang, Ellie Wen

reinforcement-learning safety

arXiv AI

Aug 12

Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness

arXiv:2605. 23146v3 Announce Type: replace-cross Abstract: Classical reinforcement learning assumes the agent interacts with a fixed environment whose behavior does not depend on the agent's policy.

By Manish Aryal, Faiyaz Azam, Agnivo Banerjee, Syed Mahir Ahamed, Sai Sidhanth Manoharan Jayanthi, Allegra Laro, Cl\'ement Legentilhomme, Andrew Lin, Florian Lorkowski, Marina P\'erez del Valle, Radman Rakhshandehroo, Patric Rommel, Emanuel Ruzak, Nathan Theng, Paul Yushin Rapoport

agents reinforcement-learning safety

arXiv AI

Aug 12

Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds

arXiv:2608. 10056v1 Announce Type: cross Abstract: Following a target human in crowded environments involves an inherent conflict between staying close to the target and navigating safely among surrounding pedestrians and obstacles.

By Shiting Gong, Jianpeng Yao, Jinfeng Wang, Marco Pavone, Jiachen Li

reinforcement-learning robotics safety

arXiv AI

Aug 12

CuSearch: Curriculum Rollout Sampling via Search Depth for Agentic RAG

arXiv:2605. 11611v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for training agentic retrieval-augmented generation (RAG) systems from outcome-only supervision.

By Jianghan Shen, Siqi Luo, Xinyu Cheng, Jing Xiong, Yue Li, Jiyao Liu, Jiashi Lin, Yirong Chen, Junjun He

rag agents reinforcement-learning

arXiv AI

Aug 12

Evaluation-Conditioned Training: Teaching Models to Generalize to Stronger Oversight Regimes

arXiv:2608. 10209v1 Announce Type: new Abstract: Feedback signals used to train Large Language Models (LLMs) are the primary driver of their behavior and our main lever for instilling alignment with human values and objectives.

By Alec Harris, Kasey Corra, Archie Chaudhury, Yixiong Hao

llms reinforcement-learning fine-tuning safety

arXiv AI

Aug 12

MIRA: Medical Image Reflection for Agentic Diagnosis

arXiv:2608. 10827v1 Announce Type: cross Abstract: Medical visual agents can use tools to inspect images and retrieve external knowledge, but indiscriminate tool use may introduce noisy or misleading evidence.

By Shengzhi Wang, Jun Yang, Kai Wu, Xiaozhong Ji, Yiwen Ye, Ziyang Chen, Mingliang Xiong, Wen Fang, Mingqing Liu, Mengyuan Xu, Miaoxuan Shan, Caiyan Liu, Bin He, Qingwen Liu

agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 12

Critic-Free Pretraining for Efficient Online Reinforcement Learning Fine-Tuning

arXiv:2608. 10473v1 Announce Type: cross Abstract: Offline-to-online (O2O) reinforcement learning aims to leverage policies pretrained on static datasets while improving them through online interaction.

By Daoyi Li, Yixian Zhang, Chao Yu, Wenbo Ding, Yu Wang

reinforcement-learning fine-tuning

arXiv AI

Aug 12

Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization

arXiv:2602. 02035v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning systems deployed in real-world robotics applications face severe communication constraints that significantly impact coordination effectiveness.

By Ahmad Farooq, Kamran Iqbal

agents reinforcement-learning robotics efficiency

arXiv AI

Aug 12

From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation

arXiv:2608. 10182v1 Announce Type: cross Abstract: Large-scale targeting and recommendation systems are typically built around predictive scores fed into heuristic or local allocation.

By Changshuai Wei, John Bencina, Phuc Nguyen, Andre Assuncao Silva T Ribeiro, Benjamin Zelditch

llms rag reinforcement-learning

arXiv AI

Aug 12

SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning

arXiv:2608. 09967v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret.

By Tamar Gozlan, Claudia V. Goldman

agents reinforcement-learning

arXiv AI

Aug 12

Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits

arXiv:2608. 10526v1 Announce Type: cross Abstract: Motivated by decentralized applications, we study cooperative multi-agent bandits in continuous (Lipschitz) action spaces when the Lipschitz constant is unknown.

By Ricardo Parada, Chenzhang Zhao, William Chang

agents reinforcement-learning efficiency

arXiv AI

Aug 12

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

arXiv:2608. 10538v1 Announce Type: new Abstract: Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution.

By Chenhao Dang, Siyuan Xiong, Conghui He, Weijia Li

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 12

InSight-doc: Agentic Visual Perception for Long-Document Understanding

arXiv:2608. 10628v1 Announce Type: cross Abstract: Long-document understanding often requires reasoning over many visually rich pages, making inference costly and prone to context rot.

By Kaican Li, Weiyan Xie, Lewei Yao, Jiannan Wu, Lanqing Hong, Yongxiang Huang, Nevin L. Zhang

agents reinforcement-learning fine-tuning efficiency benchmarks safety

arXiv AI

Aug 12

Mitigating Bus Bunching with Reinforcement Learning Enhanced by Semantic Stop Embedding

arXiv:2608. 10207v1 Announce Type: new Abstract: Bus bunching degrades service regularity and increases passenger waiting in high-frequency transit.

By Xin Dong, Vikash V. Gayah

llms rag reinforcement-learning fine-tuning

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