Zobrazeno 1 - 10
of 735
pro vyhledávání: '"Zhang Weinan"'
Crafting effective features is a crucial yet labor-intensive and domain-specific task within machine learning pipelines. Fortunately, recent advancements in Large Language Models (LLMs) have shown promise in automating various data science tasks, inc
Externí odkaz:
http://arxiv.org/abs/2410.12865
What will information entry look like in the next generation of digital products? Since the 1970s, user access to relevant information has relied on domain-specific architectures of information retrieval (IR). Over the past two decades, the advent of
Externí odkaz:
http://arxiv.org/abs/2410.09713
Autor:
Wang, Jun, Fang, Meng, Wan, Ziyu, Wen, Muning, Zhu, Jiachen, Liu, Anjie, Gong, Ziqin, Song, Yan, Chen, Lei, Ni, Lionel M., Yang, Linyi, Wen, Ying, Zhang, Weinan
In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs). OpenR unifies data acquisition, reinforcement learning training (bot
Externí odkaz:
http://arxiv.org/abs/2410.09671
Autor:
Lin, Qiqiang, Wen, Muning, Peng, Qiuying, Nie, Guanyu, Liao, Junwei, Wang, Jun, Mo, Xiaoyun, Zhou, Jiamu, Cheng, Cheng, Zhao, Yin, Zhang, Weinan
Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing their potential for executing complex tasks crucially relies on enhanceme
Externí odkaz:
http://arxiv.org/abs/2410.04587
Autor:
Hua, Pu, Liu, Minghuan, Macaluso, Annabella, Lin, Yunfeng, Zhang, Weinan, Xu, Huazhe, Wang, Lirui
Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scalability issues as many sim-to-real methods focus on a single task. To
Externí odkaz:
http://arxiv.org/abs/2410.03645
Autor:
Liu, Naming, Wang, Mingzhi, Wang, Xihuai, Zhang, Weinan, Yang, Yaodong, Zhang, Youzhi, An, Bo, Wen, Ying
The ex ante equilibrium for two-team zero-sum games, where agents within each team collaborate to compete against the opposing team, is known to be the best a team can do for coordination. Many existing works on ex ante equilibrium solutions are aimi
Externí odkaz:
http://arxiv.org/abs/2410.01575
Reinforcement Learning (RL) has shown its remarkable and generalizable capability in legged locomotion through sim-to-real transfer. However, while adaptive methods like domain randomization are expected to make policy more robust to diverse environm
Externí odkaz:
http://arxiv.org/abs/2409.17992
Autor:
Lai, Hang, Cao, Jiahang, Xu, Jiafeng, Wu, Hongtao, Lin, Yunfeng, Kong, Tao, Yu, Yong, Zhang, Weinan
Legged locomotion over various terrains is challenging and requires precise perception of the robot and its surroundings from both proprioception and vision. However, learning directly from high-dimensional visual input is often data-inefficient and
Externí odkaz:
http://arxiv.org/abs/2409.16784
Autor:
Li, Qingyao, Xia, Wei, Du, Kounianhua, Dai, Xinyi, Tang, Ruiming, Wang, Yasheng, Yu, Yong, Zhang, Weinan
LLM agents enhanced by tree search algorithms have yielded notable performances in code generation. However, current search algorithms in this domain suffer from low search quality due to several reasons: 1) Ineffective design of the search space for
Externí odkaz:
http://arxiv.org/abs/2409.09584
Reinforcement Learning has revolutionized decision-making processes in dynamic environments, yet it often struggles with autonomously detecting and achieving goals without clear feedback signals. For example, in a Source Term Estimation problem, the
Externí odkaz:
http://arxiv.org/abs/2409.09541