The First Workshop on Efficient Reasoning

Recent progress in large reasoning models (LRMs), like OpenAI o1 and Deepseek R1, has been pivotal for tackling complex applications, from mathematical and code reasoning to advanced symbolic and agentic planning. Their success often relies on test-time scaling, which involves increasing the generation length or depth. However, these approaches incur significant efficiency bottlenecks during training and inference. To overcome these limitations, further advancements are needed in data, algorithms, and systems applicable across various domains, as exemplified by work such as s1, Z1, and verl. The proposed workshop will bring together researchers and practitioners to rethink efficient reasoning under tight compute, memory, latency, throughput, and cost budgets, with the goal of translating theoretical breakthroughs into practical, deployable solutions.