SIFT paper decouples tree search from costly benchmarks to make recursive self-improving coding agents scale
burkov · x · 2026-09-24
Self-improving coding agents that recursively modify their own source hit a scalability wall: validating each candidate modification requires extensive benchmark runs costing tens of thousands of dollars in API fees and thousands of processing hours, while evaluating small subsets yields noisy signals that degrade search.
The paper introduces SIFT (Recursive Self-Improvement via Fast Tree-search), which decouples exploration from expensive benchmark evaluation, drastically cutting compute and time while achieving competitive coding performance.
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