LLMS can now resonate in parallel: UC Berkeley and UCSF scientists introduce adaptive parallel reasoning to scale -infer effectively without exceeding context windows
Large Language Models (LLMS) have made significant progress in reasoning features, exemplified by breakthrough systems such as Openai O1 and Deepsekr1, using test time calculation for search and reinforcement learning to optimize performance. Despite this progress, the current methods are facing critical challenges that prevent their effectiveness. Serialized chain-to-thinking approaches generate excessive long starting sequences, … Read more
