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

Oct 7

Reward-Driven Learning under Prompt-Level Differential Privacy

arXiv:2610.07212v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains a language model on problems that may themselves be confidential, and the trained model ca...

By Jiachen Zhao, Antonia Januszewicz, Taeho Jung

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Oct 7

Structuring MoE Expert Selection for Agentic Reinforcement Learning

arXiv:2610.07332v1 Announce Type: new Abstract: Long-horizon LLM agents are frequently implemented using sparse mixture-of-experts (MoE) models, yet the co-design of agentic behavior and MoE structur...

By Bolian Li, Ting-Yao Hu, Cheng-Yu Hsieh, Sanjoy Chowdhury, Oncel Tuzel, Raviteja Vemulapalli

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Oct 7

Adapting to Changes in Agent Behavior via Finite-Depth Policy Sensitivity

arXiv:2610.07475v1 Announce Type: new Abstract: Adapting a reinforcement learning policy to changes in another agent's behavior typically requires a large amount of new interaction data. Policy sensi...

By Lan Shi, Daigo Shishika, Xuan Wang

agents reinforcement-learning fine-tuning

arXiv Machine Learning

Oct 7

AFA-BANDIT: Provably Near-Optimal Online Multi-Feature Classification Under Budget Constraints

arXiv:2610.07615v1 Announce Type: new Abstract: Active Feature Acquisition (AFA) is a classification problem in which an agent decides which costly features to acquire before predicting each sample's...

By AbdAlRahman Odeh, Teng-Hui Huang, Hesham El Gamal

agents reinforcement-learning

arXiv Machine Learning

Oct 7

FC-SWE: Failure-Conditioned RL for Long-Horizon Software Engineering Agents

arXiv:2610.07898v1 Announce Type: new Abstract: Repository-level software engineering (SWE) is a challenging long-horizon setting: agents must reason over extended interactions, use tools, and adapt...

By Jia Liufu, Bin Hu, Linglin Jing, Terry Kong, Yuki Huang, Ashwath Aithal, Wenming Yang, Jun Yang

agents reinforcement-learning

arXiv Machine Learning

Oct 7

Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning

arXiv:2610.07910v1 Announce Type: new Abstract: Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and pe...

By SungJae Ahn, Jeong Woon Lee, Kyoleen Kwak, Hyoseok Hwang

reinforcement-learning robotics

arXiv Machine Learning

Oct 7

Enhancing Diffusion Language Models with Autoregressive Post-Training Weights

arXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders...

By Yiming Qin, Ke Wang, Amel Abdelraheem, Adam Hazimeh, Pascal Frossard

llms diffusion reinforcement-learning fine-tuning

arXiv Machine Learning

Oct 7

Decision-Focused Learning in MDPs: An Occupancy Measure Approach

arXiv:2610.08384v1 Announce Type: new Abstract: In this work, we consider decision-focused learning (DFL) for a Markov decision process (MDP), where existing methods differentiate through the KKT con...

By Zihao Zhao, Ashwath K. Karunakaram, Ali Eshragh, Yuexing Li, Kai Wang

reinforcement-learning

arXiv Machine Learning

Oct 7

Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling

arXiv:2610.08677v1 Announce Type: new Abstract: Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly...

By Nicol\`o Felicioni, Michael Benigni, Maurizio Ferrari Dacrema, Paolo Cremonesi

reinforcement-learning benchmarks

arXiv Machine Learning

Oct 7

Statistical Turbulence and High-Fidelity Disturbance Fields for Quadrotor Flight Control

arXiv:2610.06874v1 Announce Type: cross Abstract: Reinforcement-learning quadrotor controllers are usually trained under simplified wind models, yet the impact of wind-field fidelity, as opposed to m...

By Xun Huang

agents reinforcement-learning

arXiv Machine Learning

Oct 7

FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining

arXiv:2610.06947v1 Announce Type: cross Abstract: Factor mining seeks to discover signals from financial data that predict future asset returns and guide portfolio construction. Automated factor mini...

By Zhuohan Wang, Carmine Ventre

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Oct 7

BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation

arXiv:2610.07594v1 Announce Type: cross Abstract: Humanoid household manipulation requires the arms to act while the body balances, steps and changes posture. We present BiGym 2.0, an adaptation of B...

By Zexi Zhang, Zecheng Zhu, Zidong Chen, Zulkhuu Tuya, Stephen James

agents reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Oct 7

Learning Grasp Targeting from Point Clouds for Log Pile Clearing on a Hydraulic Crane

arXiv:2610.07613v1 Announce Type: cross Abstract: In mill yards, log loaders clear dense piles by a sequence of bundle grasps: hundreds of logs rest in contact, and each removal changes the pile avai...

By George Sideris, Lucas Bessai, Heshan Fernando, Elie Ayoub, Nicolas Lemieux, Inna Sharf

reinforcement-learning fine-tuning

arXiv Machine Learning

Oct 7

Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret

arXiv:2610.07737v1 Announce Type: cross Abstract: We study fair multi-armed bandits under the Nash Social Welfare (NSW) objective, which measures performance via the geometric mean of accumulated rew...

By Avishek Ghosh

reinforcement-learning safety

arXiv Machine Learning

Oct 7

Energy-Aware Path Following: Comparative Analysis of Reinforcement Learning and NMPC for Electric Vehicles

arXiv:2610.08112v1 Announce Type: cross Abstract: Path-following control strategies typically follow the bi-objective optimization dilemma: minimizing deviations from a reference path while maintaini...

By Mohamed Sabaa, Mostafa Emam

reinforcement-learning fine-tuning

arXiv Machine Learning

Oct 7

HARL-A: An Extensible Benchmark Framework for Heterogeneous Multi-Agent Adversarial Reinforcement Learning in IsaacLab

arXiv:2510.01264v2 Announce Type: replace Abstract: Progress in adversarial multi-agent reinforcement learning (MARL) for robotics has been hampered by a lack of shared, extensible infrastructure tha...

By Isaac Peterson, Christopher Allred, Jacob Morrey, Mario Harper

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Oct 7

Honesty over Accuracy: Trustworthy Language Models through Reinforced Hesitation

arXiv:2511.11500v3 Announce Type: replace Abstract: Modern language models fail a fundamental requirement of trustworthy intelligence: knowing when not to answer. Despite achieving impressive accurac...

By Mohamad Amin Mohamadi, Tianhao Wang, Zhiyuan Li

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Oct 7

Particle Monte Carlo Tree Search

arXiv:2605.08982v4 Announce Type: replace Abstract: Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement and action selection in Reinforcement Learning. Due to its sequenti...

By Yaniv Oren, Viliam Vadocz, Joery A. de Vries, Wendelin B\"ohmer, Matthijs T. J. Spaan, Hendrik Baier

llms reinforcement-learning benchmarks

arXiv Machine Learning

Oct 7

SeedER: Seed-Expand-Retrieve for Efficient Knowledge Graph Retrieval

arXiv:2605.23753v2 Announce Type: replace Abstract: Knowledge graphs (KGs) offer a rich representation for relational knowledge, but their irregular structure makes retrieval challenging: ego-graph e...

By Hamed Shirzad, Frederik Wenkel, Dominique Beaini, Danica J. Sutherland, Emmanuel Noutahi

llms rag agents reinforcement-learning fine-tuning

arXiv Machine Learning

Oct 7

EvoHarness-RL: Learning Runtime Harness Coordination for Self-Evolving Agents

arXiv:2608.05446v2 Announce Type: replace Abstract: Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, recover from failures, and reuse experie...

By Xuying Ning, Dongqi Fu, Tianxin Wei, Yuanchen Bei, Xiyuan Yang, Wujiang Xu, Yueqi Song, Bingxuan Li, Zihao Li, Hanqing Zeng, Xiang Shen, Yajuan Wang, Yifan Wu, Qifan Wang, Jiayi Liu, Hong Li, Yinglong Xia, Xiangjun Fan, Hanghang Tong, Jingrui He

llms agents reinforcement-learning

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