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.
6851dd060164cd13167103d7
King Zhu
claimed_verified
2025-06-24T09:33:07.563Z
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kang zhu
user
kangz
6851dd060164cd13167103d8
Hanhao Li
6851dd060164cd13167103d9
Siwei Wu
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Tianshun Xing
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2025-07-10T09:12:52.409Z
65d2251f98b4a470bf6a26e3
https://cdn-avatars.huggingface.co/v1/production/uploads/65d2251f98b4a470bf6a26e3/C4T0LHYGejrI9mu_k3M8p.jpeg
xts
user
xtsssss
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Dehua Ma
6851dd060164cd13167103dc
Xiangru Tang
6851dd060164cd13167103dd
Minghao Liu
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2025-06-20T12:20:10.382Z
6417d9ea8f689506e7148417
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minghao
user
Liam-Liu
6851dd060164cd13167103de
Jian Yang
6851dd060164cd13167103df
Jiaheng Liu
6851dd060164cd13167103e0
Yuchen Eleanor Jiang
6851dd060164cd13167103e1
Changwang Zhang
6851dd060164cd13167103e2
Chenghua Lin
6851dd060164cd13167103e3
Jun Wang
6851dd060164cd13167103e4
Ge Zhang
claimed_verified
2025-06-18T12:16:44.662Z
638efcf4c67af472d316d424
/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg
Ge Zhang
user
zhangysk
6851dd060164cd13167103e5
Wangchunshu Zhou
claimed_verified
2025-06-18T12:16:42.693Z
628c8598ef14f971b698107f
/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