arXiv Machine Learning

Survival Reinforcement Learning: Toward Scalable Self-Supervised RL

The paper introduces Survival Reinforcement Learning (SRL), an online classification-based method that extends the survival value learning framework to maximize an agent’s dwell time at target goals. SRL addresses limitations of contrastive reinforcement learning (CRL) in long-horizon, goal-conditioned tasks by avoiding the uniformity-tolerance dilemma and reducing undesirable bang-bang control behaviors. Across robotic benchmarks, SRL matches CRL on manipulation tasks and outperforms it by 2x to 8x on stable, long-horizon locomotion tasks, suggesting classification-based approaches are a promising direction for scaling reinforcement learning.

arXiv Machine Learning

Aug 28

Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals

The paper introduces Safe Contrastive Reinforcement Learning (Safe-CRL), a method that corrects bias in contrastive RL caused by failure-terminated Markov decision processes. By applying mass-weighted InfoNCE and a log-survival-mass score, Safe-CRL uses only a one-bit failure signal to improve survival and goal-reaching performance across twelve robot navigation and locomotion tasks. The approach demonstrates complex failure-avoidance behaviors and completes the theoretical foundation of contrastive RL under failure termination.

By Guopeng Li, Yiyang Duan, Yiru Jiao, Chengcheng Xu

reinforcement-learning robotics safety

arXiv AI

4d ago

ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning

arXiv:2609.36238v1 Announce Type: new Abstract: A goal that is close in space can be far away in time. Obstacles, terrain, and the agent's own capabilities determine how long it takes to get there. Y...

By Nico Bohlinger, Jan Peters

rag agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Aug 11

RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

arXiv:2608. 09853v1 Announce Type: cross Abstract: General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from large-scale heterogeneous corpora remains underexplored.

By Dongchi Huang, Hongyin Zhang, Bohan Hou, Siteng Huang, Zhian Su, Hang Guo, Tong Lu, Zhaofeng Xu, Jiahao Tang, Jianfei Yang, Donglin Wang, Peixi Peng, Mingxiu Chen, Deli Zhao, Xin Li

robotics

arXiv Machine Learning

Jun 11

UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning

arXiv:2606. 12372v1 Announce Type: cross Abstract: Human-in-the-loop reinforcement learning (HiL-RL) has emerged as an effective paradigm for real-world robotic manipulation, enabling online policy improvement with human guidance.

By Haoyuan Deng, Yitong Gao, Yudong Lin, Haichao Liu, Zhenyu Wu, Ziwei Wang

agents reinforcement-learning robotics benchmarks

arXiv AI

Aug 25

Beyond Success and Failure: Length-Aware Contrastive Learning for GUI Agents

arXiv:2608.21830v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) have shown strong potential for automating tasks across diver...

By Chengyang Gu, Le Zhang, Jingbo Zhou, Yize Chen, Yu Shi, Siqi Bao, Zheng-Fan Wu, Hua Wu, Hui Xiong

llms agents reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Sep 1

Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL

arXiv:2608.30640v1 Announce Type: new Abstract: While self-supervised approaches to reinforcement learning have achieved strong results by learning representations of states and actions, a key open q...

By Michal Korniak, Kamil Dybek, Benjamin Eysenbach, Marco Bagatella, Micha{\l} Bortkiewicz

rag reinforcement-learning benchmarks

arXiv Machine Learning

Aug 13

Adaptation of Generalist Robot Policies with Minimal Data

arXiv:2608. 11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction.

By Shreyas Kowshik, Sreyas Venkataraman, Leo Wang, Niharika Pant, Max Simchowitz, Aviral Kumar

agents reinforcement-learning robotics

arXiv Machine Learning

Jun 15

Provably Safe, Yet Scalable Reinforcement Learning

arXiv:2606. 14536v1 Announce Type: new Abstract: Safe reinforcement learning (RL) aims to learn policies that optimize rewards while satisfying constraints.

By Kai S. Yun, Zeyang Li, Navid Azizan

reinforcement-learning robotics safety

arXiv AI

Jul 20

SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training

arXiv:2506. 04147v5 Announce Type: replace-cross Abstract: Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators.

By Jiaheng Hu, Peter Stone, Roberto Mart\'in-Mart\'in

reinforcement-learning robotics benchmarks safety

arXiv AI

Jul 1

Stage-Transition Dense Reward Modeling for Reinforcement Learning

arXiv:2606. 31377v1 Announce Type: cross Abstract: Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards, while manually designing dense shaping signals is costly and brittle to changes in environments and object configurations.

By Yang Yang, Bingjie Chen, Zihan Wang, Yizhe Li, Guoping Pan, Yi Cheng, Houde Liu

agents reinforcement-learning robotics

arXiv Machine Learning

Jun 29

Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience

arXiv:2606. 27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations.

By Raymond Yu, William Huey, Mustafa Mukadam, Anusha Nagabandi, Abhishek Gupta

reinforcement-learning robotics efficiency

arXiv AI

Jun 19

Temporal Self-Imitation Learning

arXiv:2606. 19752v1 Announce Type: cross Abstract: Long-horizon robot manipulation policies trained with reward shaping can still exploit dense rewards through inefficient interaction, while rare efficient behaviors may be forgotten during training.

By Yinsen Jia, Boyuan Chen

reinforcement-learning robotics

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