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Inference scaling improves large language model (LLM) performance and extends to autonomous LLM agents via increased search budgets, termed search scaling. While inference scaling is well-studied, its mechanisms and limits in autonomous research remain less understood.
Inference scaling improves large language model (LLM) performance and extends to autonomous LLM agents via increased search budgets, termed search scaling. While inference scaling is well-studied, its mechanisms and limits in autonomous research remain less understood.
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Inference scaling has been shown to improve large language model (LLM) performance, and this principle naturally extends to autonomous LLM agents through increased search budgets, which we refer to as *search scaling*. Although prior work has characterized the mechanisms, scaling behavior, and performance limits...
Understanding search scaling in autonomous LLM agents can inform how to optimize their performance and efficiency. This insight is crucial for advancing autonomous quantitative factor mining and other research applications.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to arXiv.
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