arXiv Machine Learning

Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX

arXiv:2510. 01764v3 Announce Type: replace Abstract: Reinforcement learning (RL) research requires diverse, challenging environments that are both tractable and scalable.

arXiv Machine Learning

Sep 10

PlayTrain: An Efficient Reinforcement Learning Framework for LLM-Generated Adaptable JavaScript Games

arXiv:2609.09059v1 Announce Type: new Abstract: While many video-game environments (VGEs) have played crucial roles in advancing reinforcement learning (RL), developing novel VGEs or modifying existi...

By Ryan Truong, Lance Ying, Samuel J. Gershman, Kazuki Irie

llms agents reinforcement-learning

arXiv AI

Sep 10

EnvCraft: Synthesizing Executable Environments in Agentic RL for Claw-like Agent

arXiv:2609.05576v1 Announce Type: new Abstract: The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across statefu...

By Yirong Zeng, Shen You, Jinhang Feng, Yufei Liu, Xiao Ding, Yutai Hou, Hao Cong, Yuxian Wang, Wu Ning, Wang Xu, Bibo Cai

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 19

CRAX: Fast Safe Reinforcement Learning Benchmarking

arXiv:2606. 20376v1 Announce Type: cross Abstract: Safety is a core concern for deploying reinforcement learning (RL) agents in real-world domains such as robotics and autonomous driving.

By Tristan Tomilin, Mourad Boustani, Mickey Beurskens, Thiago D. Sim\~ao

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Sep 22

Streaming Deep Reinforcement Learning Finally Works

arXiv:2410.14606v3 Announce Type: replace Abstract: Learning from a stream of experience as it arrives, also known as streaming learning, is a core part of natural learning. However, reliable streami...

By Mohamed Elsayed, Elena Sorina Lupu, Gautham Vasan, A. Rupam Mahmood

reinforcement-learning robotics benchmarks

arXiv AI

Sep 23

Conduit: An Experience Data Plane for Distributed Reinforcement Learning

arXiv:2609.24456v1 Announce Type: cross Abstract: Distributed reinforcement learning (RL) scales training by parallelizing actors and learners around an Experience Buffer. As RL workloads grow, howev...

By Sitong Zhang, Tuo Shi, Mario Di Francesco, Zeke Wang, Bo Zhao

reinforcement-learning

arXiv AI

3d ago

Code to Control: Synthesizing Parameterized Reactive Controllers

arXiv:2609.38733v1 Announce Type: new Abstract: Recent LLM-based approaches to control either invoke a language model to select actions or synthesize world models that require planning at every decis...

By Zergham Ahmed, Joshua B. Tenenbaum, Chris Bates, Samuel J. Gershman

llms reinforcement-learning robotics

arXiv AI

Aug 25

MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning

arXiv:2608.22167v1 Announce Type: new Abstract: Reinforcement learning (RL) has become an effective way to improve the tool-use ability of large language models (LLMs), but most existing RL framework...

By Ziyang Luo, Yan Yang, Xiangru Jian, Ziji Shi, Xiaoqiang Lin, Jun Hao Liew, Silvio Savarese, Junnan Li

llms agents reinforcement-learning

arXiv AI

Jun 3

Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics

arXiv:2507. 21638v2 Announce Type: replace Abstract: The development of reinforcement learning (RL) algorithms has been largely driven by ambitious challenge tasks and benchmarks.

By Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius, Elle Miller, Trevor McInroe, Fan Zhang, Patricia Wollstadt, Stefano V. Albrecht, Subramanian Ramamoorthy

agents reinforcement-learning robotics benchmarks

arXiv Computer Vision

Sep 21

MintAct: A Unified Visual Agent for Digital Environments

arXiv:2609.22083v1 Announce Type: new Abstract: We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual too...

By Mingfei Gao, Rui Tian, Haiming Gang, Bohan Zhai, Le Zhang, Yuanzheng Gong, Di Feng, Ege \"Ozsoy, Kaixin Ma, Vishwesh Kirthivasan, O\u{g}uzhan Fatih Kar, Roman Bachmann, Anders Boesen Lindbo Larsen, Afshin Dehghan

llms agents reinforcement-learning multimodal benchmarks

arXiv AI

Jul 14

SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

arXiv:2607. 11185v1 Announce Type: new Abstract: Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution.

By Bowen Lv, Xiao Liu, Yanyu Ren, Hanyu Lai, Bohao Jing, Hanchen Zhang, Yanxiao Zhao, Shuntian Yao, Jie Tang, Yuxiao Dong

agents computer-vision reinforcement-learning benchmarks

arXiv Machine Learning

Jun 4

MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU

arXiv:2606. 04847v1 Announce Type: cross Abstract: Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code.

By Kun Cheng, Songshuo Lu, Sicong Liao, Tankun Li, Yafei Zhang, Dong Yang, Qiheng Lv, Hua Wang, Zhi Chen, Yaohua Tang

llms reinforcement-learning fine-tuning

arXiv AI

Jun 16

RollArt: Disaggregated Multi-Task Agentic RL Training at Scale

arXiv:2512. 22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation.

By Wei Gao, Yuheng Zhao, Tianyuan Wu, Shaopan Xiong, Weixun Wang, Dakai An, Lunxi Cao, Dilxat Muhtar, Zichen Liu, Haizhou Zhao, Ju Huang, Siran Yang, Yongbin Li, Wenbo Su, Jiamang Wang, Lin Qu, Bo Zheng, Wei Wang

llms agents reinforcement-learning efficiency

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