Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization
Existing Large Language Model (LLM) agent frameworks face two significant challenges: high configuration costs and static capabilities. Building a high-quality agent often requires extensive manual effort in tool integration and prompt engineering, while deployed agents struggle to adapt to dynamic environments without expensive fine-tuning. To address these issues, we propose Youtu-Agent, a modular framework designed for the automated generation and continuous evolution of LLM agents. Youtu-Agent features a structured configuration system that decouples execution environments, toolkits, and context management, enabling flexible reuse and automated synthesis. We introduce two generation paradigms: a Workflow mode for standard tasks and a Meta-Agent mode for complex, non-standard requirements, capable of automatically generating tool code, prompts, and configurations. Furthermore, Youtu-Agent establishes a hybrid policy optimization system: (1) an Agent Practice module that enables agents to accumulate experience and improve performance through in-context optimization without parameter updates; and (2) an Agent RL module that integrates with distributed training frameworks to enable scalable and stable reinforcement learning of any Youtu-Agents in an end-to-end, large-scale manner. Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47\%) and GAIA (72.8\%) using open-weight models. Our automated generation pipeline achieves over 81\% tool synthesis success rate, while the Practice module improves performance on AIME 2024/2025 by +2.7\% and +5.4\% respectively. Moreover, our Agent RL training achieves 40\% speedup with steady performance improvement on 7B LLMs, enhancing coding/reasoning and searching capabilities respectively up to 35\% and 21\% on Maths and general/multi-hop QA benchmarks.
69564d96832867f2535257af
Yuchen Shi
claimed_verified
2026-01-04T20:09:50.111Z
622b00a776c20fee5d14501b
/avatars/e00496dda1e309548e7b5b437839bb65.svg
Eason shi
Easonshi
user
Easonshi
69564d96832867f2535257b0
Yuzheng Cai
claimed_verified
2026-01-05T08:27:50.884Z
66e258bdc70c02b46dfed6e3
/avatars/ccc2d604616c018f45a268a610472cac.svg
Yuzheng Cai
Ucreate
user
Ucreate
69564d96832867f2535257b1
Siqi Cai
69564d96832867f2535257b2
Zihan Xu
69564d96832867f2535257b3
Lichao Chen
claimed_verified
2026-01-05T08:27:46.825Z
64154bfa385a75d7790f80e8
/avatars/9e22f54b5eb7c4ebedad99a9a92c4b6a.svg
Lichao Chen
nth233
user
nth233
69564d96832867f2535257b4
Yulei Qin
claimed_verified
2026-01-04T20:09:48.064Z
6390525c00fb8ec4a424e0ff
/avatars/4302571e2ef4a9875491221aa630a329.svg
Yulei Qin
yolay
user
yolay
69564d96832867f2535257b5
Zhijian Zhou
69564d96832867f2535257b6
Xiang Fei
claimed_verified
2026-01-06T10:02:34.077Z
6642f0940623aeacc9740c2b
https://cdn-avatars.huggingface.co/v1/production/uploads/6642f0940623aeacc9740c2b/Oxxg12Lh35T-kdWWPZCLV.png
shawn
xfey
user
xfey
69564d96832867f2535257b7
Chaofan Qiu
claimed_verified
2026-01-05T08:27:48.910Z
6604e43869c47cd78fdebd08
/avatars/4c11f5e1aeae3c5eb213f6ec6d5bfe72.svg
Qiu
ChaofanDFG
user
ChaofanDFG
69564d96832867f2535257b8
Xiaoyu Tan
claimed_verified
2026-01-05T08:27:52.763Z
637af0a7bdf7309aa6da1c36
https://cdn-avatars.huggingface.co/v1/production/uploads/637af0a7bdf7309aa6da1c36/NHZ-09otVCfbpXVxm8f-e.png
Xiaoyu Tan
WIlliam1900
user
WIlliam1900
69564d96832867f2535257b9
Gang Li
69564d96832867f2535257ba
Zongyi Li
69564d96832867f2535257bb
Haojia Lin
69564d96832867f2535257bc
Guocan Cai
69564d96832867f2535257bd
Yong Mao
69564d96832867f2535257be
Yunsheng Wu
69564d96832867f2535257bf
Ke Li
claimed_verified
2026-01-06T10:02:36.351Z
63280915eeee4dd858083092
/avatars/78347af4af42527d53e88d9969c5c934.svg
Ke Li
tristanli
user
tristanli
69564d96832867f2535257c0
Xing Sun
claimed_verified
2026-01-02T15:38:39.390Z
647401e50da364bd0d002f2a
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/vPuPn7EV092mLBOM2YZXd.png
XING SUN
tedsun
user
tedsun
Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization
Existing Large Language Model (LLM) agent frameworks face two significant challenges: high configuration costs and static capabilities. Building a high-quality agent often requires extensive manual effort in tool integration and prompt engineering, while deployed agents struggle to adapt to dynamic environments without expensive fine-tuning. To address these issues, we propose Youtu-Agent, a modular framework designed for the automated generation and continuous evolution of LLM agents. Youtu-Agent features a structured configuration system that decouples execution environments, toolkits, and context management, enabling flexible reuse and automated synthesis. We introduce two generation paradigms: a Workflow mode for standard tasks and a Meta-Agent mode for complex, non-standard requirements, capable of automatically generating tool code, prompts, and configurations. Furthermore, Youtu-Agent establishes a hybrid policy optimization system: (1) an Agent Practice module that enables agents to accumulate experience and improve performance through in-context optimization without parameter updates; and (2) an Agent RL module that integrates with distributed training frameworks to enable scalable and stable reinforcement learning of any Youtu-Agents in an end-to-end, large-scale manner. Experiments demonstrate that Youtu-Agent achieves state-of-the-art performance on WebWalkerQA (71.47\%) and GAIA (72.8\%) using open-weight models. Our automated generation pipeline achieves over 81\% tool synthesis success rate, while the Practice module improves performance on AIME 2024/2025 by +2.7\% and +5.4\% respectively. Moreover, our Agent RL training achieves 40\% speedup with steady performance improvement on 7B LLMs, enhancing coding/reasoning and searching capabilities respectively up to 35\% and 21\% on Maths and general/multi-hop QA benchmarks.