6 papers
HIL: Hybrid Imitation Learning of Diverse Parkour Skills from Videos
Jiashun Wang, Yifeng Jiang, Haotian Zhang +4
Data-driven methods leveraging deep reinforcement learning have become the dominant paradigm for developing controllers that enable physically simulated characters to produce natur…
AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading
Zheye Deng, Weixiang Yan, Changlong Yu +1
While Large Language Model (LLM) agents show promise in automated trading, they still face critical limitations. Prominent multi-agent frameworks often suffer from inefficiency, pr…
AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
Wei Fu, Jiaxuan Gao, Xujie Shen +10
Reinforcement learning (RL) has become a dominant paradigm for training large language models (LLMs), particularly for reasoning tasks. Effective RL for LLMs requires massive paral…
ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control
Jean Pierre Sleiman, He Li, Alphonsus Adu-Bredu +25
Achieving robust, human-like whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittl…
FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments
Zhi Yang, Runguo Li, Qiqi Qiang +15
Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to…
ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills
Tairan He, Jiawei Gao, Wenli Xiao +15
Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a s…