Less is more: UC Berkeley and Google unlock LLM potential through simple sampling

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new paper by researchers from Google Research and the University of California, Berkeley, demonstrates that a surprisingly simple test-time scaling approach can boost the reasoning abilities of large language models (LLMs). The key? Scaling up sampling-based search, a technique that relies on generating multiple responses and using the model itself to verify them. The core finding is that even a minimalist implementation of sampling-based search, using random sampling and self-verification, can elevate the reasoning performance of models like Gemini 1.5 Pro beyond that of o1-Preview on popular benchmarks. The findings can have important implications for enterprise applications and challenge the assumption that highly specialized training or complex architectures are always necessary for achieving top-tier performance. The limits of current test-time comput ... Read full article.