Accelerated Test-Time Scaling with Model-Free Speculative Sampling
Researchers have developed STAND (STochastic Adaptive N-gram Drafting), a new model-free speculative decoding technique designed to accelerate language model reasoning. This method leverages the redundancy in reasoning trajectories to predict tokens more efficiently without needing a separate draft model. STAND has demonstrated a 60-65% reduction in inference latency across various reasoning tasks and models, while maintaining accuracy and outperforming existing speculative decoding methods. AI
IMPACT Accelerates LLM inference speed, potentially enabling more complex reasoning tasks and wider deployment.