Beat GPT-4o at Python by searching with 100 dumb LLaMAs

Richard Sutton’s “bitter lesson” is presented as supporting not only larger-model learning but also inference-time search, which can improve smaller models by generating and evaluating many candidate outputs. In experiments using LLaMA 3.1 8B on the HumanEval Python programming benchmark, researchers used test suites as objective evaluators and found that increasing generations from one to 100 raised pass@k performance from 66.4% to 90.5%, roughly matching GPT-4o’s reported 90.2% pass@1 score, while 1,000 generations reached 95.1%. The work, run with Modal serverless GPUs, vLLM batching and caching, and isolated code-execution sandboxes for testing, reportedly cost under $50 and demonstrated predictable scaling across several orders of magnitude. The results support the broader claim that, in domains with fast and reliable outcome evaluation such as coding and formal mathematics, search can trade additional compute for reduced model-memory requirements and enable smaller open models to approach or exceed larger systems, although applying this method to subjective or open-ended language tasks remains difficult.

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