arXiv AI
Compute Allocation for Self-Evolving LLMs: From Depth-Breadth to Multi-Armed Bandits
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Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly. A natural alternative is to combine cheap and strong models under a fixed inference budget.
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COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization
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