Scaling Test-time Compute for LLM Agents

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• ATTS (Agentic Test-Time Scaling): explores test-time scaling strategies for language agents, including parallel sampling, sequential revision, verifiers and merging, and diversifying rollouts.
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• The research systematically analyzes the impact of different design strategies on agent performance, finding that scaling test-time compute improves agent capabilities.
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• Key findings include the importance of knowing when to reflect, the superiority of list-wise methods for verification and merging, and the positive effect of diversified rollouts on agent performance.
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Summarized by: Autonomous agents

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🧠💥 Want smarter language agents? Just let them think longer.

\n

This new paper puts it to the test: by scaling test-time compute (running LLMs more thoroughly), agents get significantly better at reasoning. Key takeaways:

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1️⃣ More compute = better results
2️⃣ Reflection timing is crucial
3️⃣ List-wise verification works best
4️⃣ Diverse rollouts = stronger performance

\n","updatedAt":"2025-06-18T05:51:02.473Z","author":{"_id":"638efcf4c67af472d316d424","avatarUrl":"/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg","fullname":"Ge Zhang","name":"zhangysk","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":77,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8995906710624695},"editors":["zhangysk"],"editorAvatarUrls":["/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg"],"reactions":[],"isReport":false}},{"id":"6853694bfbf3f7d151ad2da8","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false},"createdAt":"2025-06-19T01:35:07.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Strategic Scaling of Test-Time Compute: A Bandit Learning Approach](https://huggingface.co/papers/2506.12721) (2025)\n* [Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones](https://huggingface.co/papers/2505.21825) (2025)\n* [Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness](https://huggingface.co/papers/2505.22960) (2025)\n* [Scaling over Scaling: Exploring Test-Time Scaling Pareto in Large Reasoning Models](https://huggingface.co/papers/2505.20522) (2025)\n* [Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning](https://huggingface.co/papers/2506.04611) (2025)\n* [Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory](https://huggingface.co/papers/2505.10981) (2025)\n* [Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models](https://huggingface.co/papers/2506.01413) (2025)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"

This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

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The following papers were recommended by the Semantic Scholar API

\n

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• Strategic Scaling of Test-Time Compute: A Bandit Learning Approach (2025)
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• Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones (2025)
\n
• Revisiting Multi-Agent Debate as Test-Time Scaling: A Systematic Study of Conditional Effectiveness (2025)
\n
• Scaling over Scaling: Exploring Test-Time Scaling Pareto in Large Reasoning Models (2025)
\n
• Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning (2025)
\n
• Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory (2025)
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• Incentivizing Reasoning for Advanced Instruction-Following of Large Language Models (2025)
\n

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Please give a thumbs up to this comment if you found it helpful!

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If you want recommendations for any Paper on Hugging Face checkout this Space

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You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: \n\n@librarian-bot\n\t recommend

\n","updatedAt":"2025-06-19T01:35:07.104Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":318,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7167513966560364},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2506.12928","authors":[{"_id":"6851dd060164cd13167103d7","user":{"_id":"6578265ddea7e2122d02f6ba","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6578265ddea7e2122d02f6ba/Bh6JjoVF5ceLSjV7Z7nTk.jpeg","isPro":false,"fullname":"king zhu","user":"kangz","type":"user"},"name":"King Zhu","status":"claimed_verified","statusLastChangedAt":"2025-06-24T09:33:07.563Z","hidden":false},{"_id":"6851dd060164cd13167103d8","name":"Hanhao Li","hidden":false},{"_id":"6851dd060164cd13167103d9","user":{"_id":"656d97b10bbc114fe64a96c5","avatarUrl":"/avatars/fd23bae1d85c5b96c42064a5ddcfad41.svg","isPro":false,"fullname":"SiweiWu","user":"SiweiWu","type":"user"},"name":"Siwei Wu","status":"claimed_verified","statusLastChangedAt":"2025-08-13T07:30:39.488Z","hidden":false},{"_id":"6851dd060164cd13167103da","user":{"_id":"65d2251f98b4a470bf6a26e3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65d2251f98b4a470bf6a26e3/C4T0LHYGejrI9mu_k3M8p.jpeg","isPro":false,"fullname":"xts","user":"xtsssss","type":"user"},"name":"Tianshun Xing","status":"claimed_verified","statusLastChangedAt":"2025-07-10T09:12:52.409Z","hidden":false},{"_id":"6851dd060164cd13167103db","name":"Dehua Ma","hidden":false},{"_id":"6851dd060164cd13167103dc","name":"Xiangru Tang","hidden":false},{"_id":"6851dd060164cd13167103dd","user":{"_id":"6417d9ea8f689506e7148417","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6417d9ea8f689506e7148417/bAYcruWNw4WvmuQcGgcwC.jpeg","isPro":false,"fullname":"minghao","user":"Liam-Liu","type":"user"},"name":"Minghao Liu","status":"claimed_verified","statusLastChangedAt":"2025-06-20T12:20:10.382Z","hidden":false},{"_id":"6851dd060164cd13167103de","name":"Jian Yang","hidden":false},{"_id":"6851dd060164cd13167103df","name":"Jiaheng Liu","hidden":false},{"_id":"6851dd060164cd13167103e0","name":"Yuchen Eleanor Jiang","hidden":false},{"_id":"6851dd060164cd13167103e1","user":{"_id":"67ea7f597382053ae1ff676f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/67ea7f597382053ae1ff676f/zy6b17SFRBfxZvTnq5m3Z.jpeg","isPro":false,"fullname":"Changwang ZHANG","user":"mLeoKing","type":"user"},"name":"Changwang Zhang","status":"claimed_verified","statusLastChangedAt":"2025-09-06T10:57:45.954Z","hidden":false},{"_id":"6851dd060164cd13167103e2","name":"Chenghua Lin","hidden":false},{"_id":"6851dd060164cd13167103e3","name":"Jun Wang","hidden":false},{"_id":"6851dd060164cd13167103e4","user":{"_id":"638efcf4c67af472d316d424","avatarUrl":"/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg","isPro":false,"fullname":"Ge Zhang","user":"zhangysk","type":"user"},"name":"Ge Zhang","status":"claimed_verified","statusLastChangedAt":"2025-06-18T12:16:44.662Z","hidden":false},{"_id":"6851dd060164cd13167103e5","user":{"_id":"628c8598ef14f971b698107f","avatarUrl":"/avatars/3a4ad87e6b5f9e836a1160d869df1447.svg","isPro":false,"fullname":"Zhou","user":"Wangchunshu","type":"user"},"name":"Wangchunshu Zhou","status":"claimed_verified","statusLastChangedAt":"2025-06-18T12:16:42.693Z","hidden":false}],"publishedAt":"2025-06-15T17:59:47.000Z","submittedOnDailyAt":"2025-06-18T04:21:02.464Z","title":"Scaling Test-time Compute for LLM Agents","submittedOnDailyBy":{"_id":"638efcf4c67af472d316d424","avatarUrl":"/avatars/97a57859d7d87a3a8f1bb41d32a72bc2.svg","isPro":false,"fullname":"Ge Zhang","user":"zhangysk","type":"user"},"summary":"Scaling test time compute has shown remarkable success in improving the\nreasoning abilities of large language models (LLMs). In this work, we conduct\nthe first systematic exploration of applying test-time scaling methods to\nlanguage agents and investigate the extent to which it improves their\neffectiveness. Specifically, we explore different test-time scaling strategies,\nincluding: (1) parallel sampling algorithms; (2) sequential revision\nstrategies; (3) verifiers and merging methods; (4)strategies for diversifying\nrollouts.We carefully analyze and ablate the impact of different design\nstrategies on applying test-time scaling on language agents, and have follow\nfindings: 1. Scaling test time compute could improve the performance of agents.\n2. Knowing when to reflect is important for agents. 3. Among different\nverification and result merging approaches, the list-wise method performs best.\n4. Increasing diversified rollouts exerts a positive effect on the agent's task\nperformance.","upvotes":63,"discussionId":"6851dd060164cd13167103e6","ai_summary":"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.","ai_keywords":["parallel sampling algorithms","sequential revision strategies","verifiers","merging methods","diversified rollouts","test-time scaling","large language models"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"67337dffe77108f3cce35005","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/67337dffe77108f3cce35005/2mlcK-k7G6UgijO2J3QoP.jpeg","isPro":false,"fullname":"yunwenLi","user":"JunoLi622","type":"user"},{"_id":"64c910233d5a0dfed5ce5abb","avatarUrl":"/avatars/8c73f380219c05ae7e7c2fad75a570d8.svg","isPro":true,"fullname":"dma2077","user":"dma2077","type":"user"},{"_id":"63299f93688ad82b783aaf20","avatarUrl":"/avatars/7c11e60e551ef1c62aa2862529e357f5.svg","isPro":false,"fullname":"zhongyuan peng","user":"happzy2633","type":"user"},{"_id":"656d97b10bbc114fe64a96c5","avatarUrl":"/avatars/fd23bae1d85c5b96c42064a5ddcfad41.svg","isPro":false,"fullname":"SiweiWu","user":"SiweiWu","type":"user"},{"_id":"65377c30e48353201e6fdda0","avatarUrl":"/avatars/a8f803b6f2e598eaee9c52c0d2ddfc16.svg","isPro":false,"fullname":"Jiaheng Liu","user":"CheeryLJH","type":"user"},{"_id":"64ba096e760936217a3ad2e2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64ba096e760936217a3ad2e2/aNQK83Jg5PsBkY0UDg-RA.jpeg","isPro":false,"fullname":"Linzheng Chai","user":"Challenging666","type":"user"},{"_id":"647c4c901f878439e2fd34d6","avatarUrl":"/avatars/b4399d210d7239d4662b11a4ee7b527d.svg","isPro":false,"fullname":"Ziniu Li","user":"ziniuli","type":"user"},{"_id":"654907a4a1faff97850c4eff","avatarUrl":"/avatars/458c90151614bc7f116943b6e67d6b8a.svg","isPro":false,"fullname":"du","user":"dododododo","type":"user"},{"_id":"6578265ddea7e2122d02f6ba","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6578265ddea7e2122d02f6ba/Bh6JjoVF5ceLSjV7Z7nTk.jpeg","isPro":false,"fullname":"king zhu","user":"kangz","type":"user"},{"_id":"628c8598ef14f971b698107f","avatarUrl":"/avatars/3a4ad87e6b5f9e836a1160d869df1447.svg","isPro":false,"fullname":"Zhou","user":"Wangchunshu","type":"user"},{"_id":"6149a9e95347647e6bb68882","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6149a9e95347647e6bb68882/Jddln1FxScCeVgTSCNBpr.png","isPro":false,"fullname":"Zekun Moore Wang","user":"ZenMoore","type":"user"},{"_id":"620783f24e28382272337ba4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620783f24e28382272337ba4/zkUveQPNiDfYjgGhuFErj.jpeg","isPro":false,"fullname":"GuoLiangTang","user":"Tommy930","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":2}">