
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