Test Time Compute: Sampling and Chain of Thought Techniques
Explore advanced inference techniques for large language models in this comprehensive 55-minute video lecture. Dive into the differences between training and test-time compute, and learn why investing more resources in inference can be beneficial. Discover how to use verifiers for selecting optimal answers and explore methods for critiquing responses during inference. Gain a deep understanding of temperature in sampling, including when to use zero or non-zero values, and explore alternatives like beam search, top-p, top-k, and min-p sampling. Learn to implement chain-of-thought reasoning and see practical demonstrations using datasets like GSM8K and HotpotQA. Follow along with notebook run-throughs, learn to use VLLM for efficient batching, and discover techniques for scoring and grading responses. Analyze the results of various sampling and chain-of-thought configurations through detailed charts, and receive expert recommendations on optimizing these parameters for different tasks.