Self Improvement via Fast Tree-search

The paper introduces SIFT, a sample‑efficient framework for self‑improvement of coding agents that uses a fast tree‑search guided by an LLM‑as‑a‑judge signal. By performing pairwise comparisons of candidate patches and aggregating results with a regularized Bradley‑Terry model, SIFT limits expensive downstream evaluations to only the most promising nodes. The approach achieves higher coding performance on the Polyglot benchmark while reducing CPU hours, wall‑clock time, and API costs compared to prior tree‑search self‑evolution methods.

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• llms
• agents
• benchmarks