Scaling Test-time Compute for LLM Agents
Scaling test time compute has shown remarkable success in improving the
reasoning abilities of large language models (LLMs). In this work, we conduct
the first systematic exploration of applying test-time scaling methods to
language agents and investigate the extent to which it improves their
effectiveness. Specifically, we explore different test-time scaling strategies,
including: (1) parallel sampling algorithms; (2) sequential revision
strategies; (3) verifiers and merging methods; (4)strategies for diversifying
rollouts.We carefully analyze and ablate the impact of different design
strategies on applying test-time scaling on language agents, and have follow
findings: 1. Scaling test time compute could improve the performance of agents.
2. Knowing when to reflect is important for agents. 3. Among different
verification and result merging approaches, the list-wise method performs best.
4. Increasing diversified rollouts exerts a positive effect on the agent's task
performance.
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King Zhu
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2025-06-24T09:33:07.563Z
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kang zhu
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kangz
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Hanhao Li
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Siwei Wu
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2025-08-13T07:30:39.488Z
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SiweiWu
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SiweiWu
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Tianshun Xing
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2025-07-10T09:12:52.409Z
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xts
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Dehua Ma
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Xiangru Tang
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Minghao Liu
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2025-06-20T12:20:10.382Z
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minghao
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Liam-Liu
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Jian Yang
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Jiaheng Liu
6851dd060164cd13167103e0
Yuchen Eleanor Jiang
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Changwang Zhang
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2025-09-06T10:57:45.954Z
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Changwang ZHANG
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mLeoKing
6851dd060164cd13167103e2
Chenghua Lin
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Jun Wang
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Ge Zhang
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2025-06-18T12:16:44.662Z
638efcf4c67af472d316d424
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Ge Zhang
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zhangysk
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Wangchunshu Zhou
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2025-06-18T12:16:42.693Z
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/avatars/3a4ad87e6b5f9e836a1160d869df1447.svg
Zhou
user
Wangchunshu
Scaling Test-time Compute for LLM Agents
Scaling test time compute has shown remarkable success in improving the
reasoning abilities of large language models (LLMs). In this work, we conduct
the first systematic exploration of applying test-time scaling methods to
language agents and investigate the extent to which it improves their
effectiveness. Specifically, we explore different test-time scaling strategies,
including: (1) parallel sampling algorithms; (2) sequential revision
strategies; (3) verifiers and merging methods; (4)strategies for diversifying
rollouts.We carefully analyze and ablate the impact of different design
strategies on applying test-time scaling on language agents, and have follow
findings: 1. Scaling test time compute could improve the performance of agents.
2. Knowing when to reflect is important for agents. 3. Among different
verification and result merging approaches, the list-wise method performs best.
4. Increasing diversified rollouts exerts a positive effect on the agent's task
performance.
Systematic exploration of test-time scaling methods in large language agents reveals that computational scaling improves performance, especially through parallel sampling, sequential revision, effective verification, and increased rollout diversity.
parallel sampling algorithms sequential revision strategies verifiers merging methods diversified rollouts test-time scaling large language models