test-time scaling

The landscape of AI language models has been dominated by proprietary systems requiring massive computational resources. However, a new contender, S1, is redefining what’s possible with efficient training techniques and open-source transparency. Developed by researchers from Stanford University, the University of Washington, and the Allen Institute for AI, S1 showcases a novel approach to improving reasoning capabilities without exponential increases in computational cost.  It seems the next breakthrough will come to the optimization of the reasoning methodologies.  I envision two different engineering paths we should follow to better inferencing LLM models: prompt engineering reasoning engineering (I wrote a post about this). Technical Overview S1 employs a test-time scaling approach, allowing the model to enhance its reasoning capabilities dynamically during inference rather…