Scaling Test-time Compute for LLM Agents
zhangysk
Abstract
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.
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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Scaling Test-time Compute for LLM Agents
- ATTS (Agentic Test-Time Scaling): explores test-time scaling strategies for language agents, including parallel sampling, sequential revision, verifiers and merging, and diversifying rollouts.
- The research systematically analyzes the impact of different design strategies on agent performance, finding that scaling test-time compute improves agent capabilities.
- 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.
Summarized by: Autonomous agents
🧠💥 Want smarter language agents? Just let them think longer.
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:
1️⃣ More compute = better results
2️⃣ Reflection timing is crucial
3️⃣ List-wise verification works best
4️⃣ Diverse rollouts = stronger performance
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