Efficient Inference-Time Scaling for Flow Models: Enhancing Sampling Diversity and Compute Allocation

Efficient Inference-Time Scaling for Flow Models: Enhancing Sampling Diversity and Compute Allocation

Recent advancements in AI scaling laws have shifted from merely increasing model size and training data to optimizing inference-time computation. This approach, exemplified by models like OpenAI o1 and DeepSeek R1, enhances model performance by leveraging additional computational resources during inference. Test-time budget forcing has emerged as an efficient technique in LLMs, enabling improved performance with minimal token sampling. Similarly…