A new paper by researchers from Google Research and UC Berkeley shows that a simple sampling-based search approach can enhance the reasoning abilities of large language models (LLMs) without needing specialized training or complex architectures.
Researchers at UC Berkeley have developed Sky-T1-32B, an open-source reasoning-focused language model trained for less than $450, which surpasses OpenAI's o1 in benchmarks like Math500, AIME, and Livebench. This model uses optimized training processes to balance computational efficiency with robust performance, making it accessible to a broader audience and fostering inclusivity in AI research.
Pretrained Transformers as Universal Computation Engines
