arXiv AI June 2, 2026 By Vignesh Subramanian, Subhajit Roy, Suguman Bansal

Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications

arXiv:2606. 00838v1 Announce Type: new Abstract: Inductive generalization is a framework for reinforcement learning (RL) generalization in which inductively related task instances admit inductively related policies.

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reinforcement-learning

arXiv Machine Learning

Aug 24

Decoupling Policy Extraction for Offline Reinforcement Learning

arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...

By Xuyao Lin, Yixiang Shan, Jinru Duan, Tao Yang, Xinyu Zhao, Runyu Lei, Yiming Zhao, Jiaxin Fan, Zongbao Feng, Peng Jia

rag reinforcement-learning

arXiv AI

Jun 2

Certificate-Guided Evaluation of Reinforcement Learning Generalization

arXiv:2606. 00840v1 Announce Type: new Abstract: This work presents a logic-driven framework to evaluate the performance of reinforcement learning (RL) algorithms in their ability to generalize to unseen tasks.

By Vignesh Subramanian, {\DJ}or{\dj}e \v{Z}ikeli\'c, Suguman Bansal

reinforcement-learning benchmarks

arXiv AI

Jul 2

Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL

arXiv:2607. 00392v1 Announce Type: cross Abstract: Unsupervised Reinforcement Learning (URL) aims to pre-train scalable, skill-conditioned policies without extrinsic rewards, serving as a foundation for downstream control tasks.

By Jongchan Park, Seungjun Oh, Seungho Baek, Yusung Kim

agents reinforcement-learning

arXiv AI

Aug 5

Agentic Reinforcement Learning with Self-Distilled Reward Shaping

arXiv:2608. 03223v1 Announce Type: cross Abstract: Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying which intermediate decisions deserve credit.

By Ranxu Zhang, Guinan Chen, Chenshaodong, Jinghao Lin, Xiaozhou Xu, Sunzhe, Yanyong Zhang, Chao Wang

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 21

Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR

arXiv:2509. 02522v3 Announce Type: replace-cross Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have empowered large language models (LLMs) to tackle challenging reasoning tasks such as mathematics and programming, however existing RLVR methods often suffer from sparse reward signals and unstable policy gradient updates inherent to RL-based approaches.

By Jiaming Li, Longze Chen, Ze Gong, Yukun Chen, Lu Wang, Wanwei He, Run Luo, Min Yang

llms reinforcement-learning

arXiv Machine Learning

Jun 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters

reinforcement-learning robotics fine-tuning

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Related stories

Decoupling Policy Extraction for Offline Reinforcement Learning

Certificate-Guided Evaluation of Reinforcement Learning Generalization

Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL

Agentic Reinforcement Learning with Self-Distilled Reward Shaping

Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards