Decocted Experience Improves Test-Time Inference in LLM Agents
📰 ArXiv cs.AI
Decocted experience improves test-time inference in LLM agents by optimizing computation allocation
Action Steps
- Identify areas where test-time scaling can be optimized
- Apply decocted experience to allocate computation more efficiently
- Evaluate the impact on performance and cost
- Refine the approach based on results
Who Needs to Know This
AI engineers and ML researchers can benefit from this approach to improve the performance of LLM agents without updating model parameters, and product managers can apply this to optimize resource allocation in AI-powered products
Key Insight
💡 Decocted experience can optimize computation allocation during test-time inference, leading to improved performance and reduced waste
Share This
💡 Decocted experience boosts LLM agent performance without updating model params!
Key Takeaways
Decocted experience improves test-time inference in LLM agents by optimizing computation allocation
Full Article
Title: Decocted Experience Improves Test-Time Inference in LLM Agents
Abstract:
arXiv:2604.04373v1 Announce Type: new Abstract: There is growing interest in improving LLMs without updating model parameters. One well-established direction is test-time scaling, where increased inference-time computation (e.g., longer reasoning, sampling, or search) is used to improve performance. However, for complex reasoning and agentic tasks, naively scaling test-time compute can substantially increase cost and still lead to wasted budget on suboptimal exploration. In this paper, we explor
Abstract:
arXiv:2604.04373v1 Announce Type: new Abstract: There is growing interest in improving LLMs without updating model parameters. One well-established direction is test-time scaling, where increased inference-time computation (e.g., longer reasoning, sampling, or search) is used to improve performance. However, for complex reasoning and agentic tasks, naively scaling test-time compute can substantially increase cost and still lead to wasted budget on suboptimal exploration. In this paper, we explor
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