arXiv AI

Specifying Reward Functions for RL Without Environment Sampling

arXiv AI

Jun 2

Value-Free Policy Optimization via Reward Partitioning

arXiv:2506. 13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by directly leveraging scalar feedback.

By Bilal Faye, Hanane Azzag, Mustapha Lebbah

llms reinforcement-learning fine-tuning

arXiv AI

Jun 30

ProSpec RL: Plan Ahead, then Execute

arXiv:2407. 21359v2 Announce Type: replace-cross Abstract: Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition.

By Liangliang Liu, Yi Guan, BoRan Wang, Rujia Shen, Yi Lin, Chaoran Kong, Lian Yan, Jingchi Jiang

agents reinforcement-learning benchmarks

arXiv AI

Jul 16

Learning Safe Agent Behaviour from Human Preferences and Justifications via World Models

arXiv:2607. 13172v1 Announce Type: new Abstract: We address the problem of safely training an agent policy and deploying a good and safe policy, in settings where the environment dynamics are unknown and no suitable reward function is available.

By Ilias Kazantzidis, Timothy J. Norman, Yali Du, Christopher T. Freeman

agents reinforcement-learning safety

arXiv AI

Aug 25

Scaling Curriculum Learning For Autonomous Driving

arXiv:2608.22549v1 Announce Type: new Abstract: Batched simulators for autonomous driving have recently enabled training reinforcement learning (RL) agents at scale, encompassing thousands of traffic...

By Cevahir Koprulu, David Paz, Feng Tao, Yuliang Guo, Xinyu Huang, Ufuk Topcu, Liu Ren

agents reinforcement-learning robotics efficiency benchmarks

arXiv AI

Jul 7

Adaptive Margin RLHF via Preference over Preferences

arXiv:2509. 22851v4 Announce Type: replace-cross Abstract: Margin-based optimization is fundamental to improving generalization and robustness in classification tasks.

By Yaswanth Chittepu, Prasann Singhal, Greg Durrett, Scott Niekum

reinforcement-learning safety

arXiv Machine Learning

Jul 20

Dichotomous Diffusion Policy Optimization

arXiv:2601. 00898v3 Announce Type: replace Abstract: Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inference.

By Ruiming Liang, Yinan Zheng, Kexin Zheng, Tianyi Tan, Jianxiong Li, Liyuan Mao, Zhihao Wang, Guang Chen, Hangjun Ye, Jingjing Liu, Jinqiao Wang, Xianyuan Zhan

agents diffusion reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jul 1

Freeform Preference Learning for Robotic Manipulation

arXiv:2606. 32027v1 Announce Type: cross Abstract: Reward design remains a central bottleneck for autonomous robot policy improvement, especially in long-horizon manipulation tasks where sparse success labels provide too little signal and binary preferences collapse many competing notions of quality into one ambiguous signal.

By Marcel Torne, Anubha Mahajan, Abhijnya Bhat, Chelsea Finn

agents computer-vision reinforcement-learning robotics safety

arXiv AI

Aug 12

Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning

arXiv:2608. 10403v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown promising performance in autonomous driving, yet ensuring the safety of online RL policies remains challenging due to insufficient exposure to safety-critical driving scenes.

By Xincong Hu (Nanjing University), Lei Ou (Nanjing University), Maosen Li (Yinwang Intelligent Technology Co., Ltd), Jingtao Zhang (Yinwang Intelligent Technology Co., Ltd), Liguo Hou (Yinwang Intelligent Technology Co., Ltd), Zongzhang Zhang (Nanjing University)

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Jun 29

When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?

arXiv:2605. 30719v2 Announce Type: replace-cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.

By Stephane Hatgis-Kessell, Emma Brunskill

llms reinforcement-learning robotics

arXiv Machine Learning

Jun 16

CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving

arXiv:2510. 12560v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models trained with imitation learning (IL) often generalize poorly, particularly in long-tail scenarios where expert demonstrations are sparse.

By Xiaoji Zheng, Ziyuan Yang, Yanhao Chen, Yuhang Peng, Yuanrong Tang, Gengyuan Liu, Bokui Chen, Jiangtao Gong

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jun 16

Evolutionary Bilevel Reward Shaping for Generalization in Reinforcement Learning

arXiv:2606. 16236v1 Announce Type: new Abstract: Reinforcement learning (RL) often suffers from performance degradation when deployed in environments that differ from those encountered during training.

By Ekasit Usaratniwart, Xilin Gao, Marc Ong, Youhei Akimoto

agents reinforcement-learning safety

arXiv AI

Jun 26

Automating Potential-based Reward Shaping with Vision Language Model Guidance

arXiv:2606. 27180v1 Announce Type: cross Abstract: Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly attribute the sparse success rewards to relevant parts of the trajectory.

By Henrik M\"uller, Daniel Kudenko

llms agents reinforcement-learning multimodal

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