EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution
We propose EvoAlloc, a self-evolving resource-allocation agent that learns from search experience to revise its strategy for allocating computational resources across candidates.
ProofPaper ↗
Key points
- LLM-based program evolution relies on evaluation feedback to guide the iterative search for high-performing programs.
- Existing LLM-based methods typically rely on fixed allocation strategies throughout the search, potentially wasting resources on low-value candidates while overlooking promising ones.
- EvoAlloc periodically consolidates prior search and allocation outcomes into reusable experience, which informs subsequent strategy revisions.
- Across coding and agent-harness optimization benchmarks, EvoAlloc requires 59-82% fewer full evaluations and 61-89% fewer total LLM tokens to reach baseline-level performance.
Sources (1)
- [1]EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program EvolutionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 02:57 PM
We propose EvoAlloc, a self-evolving resource-allocation agent that learns from search experience to revise its strategy for allocating computational resources across candidates.
LLM-based program evolution relies on evaluation feedback to guide the iterative search for high-performing programs.
Extractive summary: sentences quoted from the sources.