UCSF
LLMs Can Now Reason in Parallel: UC Berkeley and UCSF Researchers Introduce Adaptive Parallel Reasoning to Scale Inference Efficiently Without Exceeding Context Windows
[ad_1] Large language models (LLMs) have made significant strides in reasoning capabilities, exemplified by breakthrough systems like OpenAI o1 and DeepSeekR1, which utilize test-time compute for search and reinforcement learning to optimize performance. Despite this progress, current methodologies face critical challenges that impede their effectiveness. Serialized chain-of-thought approaches generate excessively long output sequences, increasing latency…