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 18

Robust Regularized Policy Iteration under Transition Uncertainty

arXiv:2603. 09344v3 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift.

By Hongqiang Lin, Zhenghui Fu, Weihao Tang, Pengfei Wang, Yiding Sun, Qixian Huang, Dongxu Zhang

reinforcement-learning benchmarks

arXiv AI

Jun 18

DeFAb: A Verifiable Benchmark for Defeasible Abduction in Foundation Models

arXiv:2606. 18557v1 Announce Type: new Abstract: A rule-based logic solver resolves every instance in our benchmark in under 50 microseconds with 100% accuracy; the best frontier language model reaches 65% at best and drops to 23.

By Patrick Cooper, Alvaro Velasquez

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 18

Reinforcement Learning for Accelerated Aerodynamic Shape Optimisation

arXiv:2507. 17786v2 Announce Type: replace Abstract: We introduce a reinforcement learning (RL) based adaptive optimization algorithm for aerodynamic shape optimization focused on dimensionality reduction.

By Florian Sobieczky, Alfredo Lopez, Erika Dudkin, Christopher Lackner, Matthias Hochsteger, Bernhard Scheichl, Helmut Sobieczky

reinforcement-learning

arXiv Machine Learning

Jun 18

Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction

arXiv:2606. 18503v1 Announce Type: new Abstract: Remaining useful life (RUL) estimation is central to predictive maintenance, where an unplanned failure can cost far more than the asset itself.

By Manoranjan Gandhudi, Arunkumar V., G. R. Anil, Gangadharan G. R

rag reinforcement-learning benchmarks

arXiv Machine Learning

Jun 18

Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing

arXiv:2606. 12816v2 Announce Type: replace-cross Abstract: Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors.

By Yash Vardhan Tomar, Dheeraj Peddireddy

reinforcement-learning

arXiv AI

Jun 18

R2D-RL: A RoboCup 2D Soccer Environment for Multi-Agent Reinforcement Learning

arXiv:2606. 18786v1 Announce Type: new Abstract: Robot soccer is a challenging testbed for multi-agent reinforcement learning because it combines partial observability, cooperative and adversarial interaction, sparse rewards, and long-horizon tactical behavior.

By Haobin Qin, Baofeng Zhang, Hidehisa Akiyama, Keisuke Fujii

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Jun 18

MemRerank: Preference Memory for Personalized Product Reranking

arXiv:2603. 29247v3 Announce Type: replace-cross Abstract: LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch.

By Zhiyuan Peng, Xuyang Wu, Huaixiao Tou, Yi Fang, Yu Gong

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jun 18

PatchWorld: Gradient-Free Optimization of Executable World Models

arXiv:2605. 30880v2 Announce Type: replace-cross Abstract: Text-agent environments are typically modeled as partially observable Markov decision processes (POMDPs), assuming that the simulator's latent state and transition dynamics are hidden from the agent.

By Jiaxin Bai, Yue Guo, Yifei Dong, Jiaxuan Xiong, Tianshi Zheng, Yixia Li, Tianqing Fang, Yufei Li, Yisen Gao, Haoyu Huang, Zhongwei Xie, Hong Ting Tsang, Zihao Wang, Lihui Liu, Jeff Z. Pan, Yangqiu Song

llms agents reinforcement-learning safety

arXiv AI

Jun 18

ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch

arXiv:2606. 18803v1 Announce Type: new Abstract: Bringing Large Language Models (LLMs) into industrial ride-hailing dispatch as semantic feature extractors over platform-scale behavioral logs is a compelling but under-explored data systems problem.

By Tengfei Lyu, Zirui Yuan, Xu Liu, Kai Wan, Zihao Lu, Li Ma, Hao Liu

llms agents reinforcement-learning fine-tuning data-engineering

arXiv AI

Jun 18

InfoPO: Information-Driven Policy Optimization for User-Centric Agents

arXiv:2603. 00656v2 Announce Type: replace Abstract: Real-world user requests to LLM agents are often underspecified.

By Fanqi Kong, Jiayi Zhang, Mingyi Deng, Chenglin Wu, Yuyu Luo, Bang Liu

llms agents reinforcement-learning

arXiv AI

Jun 18

STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability

arXiv:2606. 19236v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from policy entropy collapse during training.

By Haipeng Luo, Qingfeng Sun, Songli Wu, Can Xu, Wenfeng Deng, Han Hu, Yansong Tang

llms agents reinforcement-learning

arXiv Machine Learning

Jun 18

GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents

arXiv:2511. 00802v2 Announce Type: replace-cross Abstract: With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies.

By Jie JW Wu, Ayanda Patrick Herlihy, Ahmad Saleem Mirza, Ali Afoud, Fatemeh Fard

llms agents reinforcement-learning robotics benchmarks

arXiv AI

Jun 18

LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents

arXiv:2606. 18388v1 Announce Type: cross Abstract: RL post-training strategies are dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically across stages, while regularization parameters predominantly oscillate in response to shifting training dynamics.

By Haoyang Fang, Wei Zhu, Boran Han, Alex Zhang, Zhenyu Pan, Shuo Yang, Shuai Zhang, Jiading Gai, Peng Tang, Cuixiong Hu, Xuan Zhu, Huzefa Rangwala, George Karypis, Bernie Wang

llms agents reinforcement-learning

arXiv Machine Learning

Jun 18

Context-Aware Optimization of Follow-Up Intervals for Type 2 Diabetes Care Using Markov Decision Processes

arXiv:2606. 19092v1 Announce Type: cross Abstract: Chronic disease management relies on regular patient-provider interactions to follow-up on disease progression and control.

By Parisa Lotfibagha, Kristen Miller, William J. Gallagher, Elizabeth B. Selden, Muge Capan

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 18

REVES: REvision and VErification--Augmented Training for Test-Time Scaling

arXiv:2606. 18910v1 Announce Type: new Abstract: Test-time scaling via sequential revision has emerged as a powerful paradigm for enhancing Large Language Model (LLM) reasoning.

By Yuanxin Liu, Ruida Zhou, Xinyan Zhao, Amr Sharaf, Hongzhou Lin, Arijit Biswas, Mohammad Ghavamzadeh, Zhaoran Wang, Mingyi Hong

llms reinforcement-learning benchmarks

arXiv AI

Jun 18

RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents

arXiv:2606. 19047v1 Announce Type: new Abstract: Multi-turn tool-use RL is bottlenecked by the rapid depletion of informative samples in static datasets.

By Ruishan Fang, Siyuan Lu, Chenyi Zhuang, Tao Lin

agents reinforcement-learning

arXiv AI

Jun 18

Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion

arXiv:2606. 18469v1 Announce Type: cross Abstract: Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms.

By Somjit Nath, Jackson J Cone, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

rag reinforcement-learning benchmarks

arXiv AI

Jun 18

Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits

arXiv:2606. 18785v1 Announce Type: cross Abstract: Identifying Pareto optimal solutions is critical to support multi-objective decision-making.

By Lennert Saerens, Bram Silue, Eleni Litsa, Peter Vrancx, Pieter Libin

reinforcement-learning benchmarks

arXiv AI

Jun 18

Retell, Reward, Repeat: Reinforcement Learning for Narrative Theory-Informed Story Retelling

arXiv:2601. 17226v2 Announce Type: replace-cross Abstract: Counterfactual story retelling exposes LLM shortcomings in constrained narrative solution spaces where they can no longer rely on recalling memorised training data.

By David Y. Liu, Xanthe Muston, Dipankar Srirag, Aditya Joshi, Sebastian Sequoiah-Grayson

llms nlp reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 18

Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation

arXiv:2606. 18844v1 Announce Type: new Abstract: Self-distillation improves reasoning in large language models by using the model's own rollouts as training signal, typically through implicit logit-level alignment that minimizes KL divergence toward a privileged target distribution.

By Zhilin Huang, Hang Gao, Ziqiang Dong, Yuan Chen, Yifeng Luo, Chujun Qin, Jingyi Wang, Yang Yang, Guanjun Jiang

llms reinforcement-learning efficiency safety

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