2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…