Build A Reasoning Model From Scratch 5: Inference Scaling 2 (Logprob Scoring, Self-Refinement)

This second video on inference-time scaling, introduces different scoring functions to break ties in majority voting and also introduces self-refinement. - Reasoning playlist: https://www.youtube.com/watch?v=Kh9mqTzjuEQ&list=PLfrHAQCRxtUs - Reasoning Book: https://amzn.to/4aAKiFY - Reasoning GitHub repo: https://github.com/rasbt/reasoning-from-scratch - LLMs from Scratch book: https://amzn.to/4fqvn0D - LLMs from Scratch repo: https://github.com/rasbt/LLMs-from-scratch - LLMs from Scratch playlist: https://www.youtube.com/watch?v=yAcWnfsZhzo&list=PLTKMiZHVd_2IIEsoJrWACkIxLRdfMlw11&index=1 00:00 Introduction and inference-time scaling recap 05:02 Loading the pretrained LLM 08:00 Comparing and scoring model answers 10:18 Building a rule-based scorer 17:53 Token probabilities and sequence likelihood 26:47 Computing token probabilities in PyTorch 30:12 Token indexing and shifted targets 37:27 Log probabilities and numerical stability 45:57 Scoring answers with average log probabilities 56:24 How self-refinement works 59:07 Generating critiques and revised answers 1:01:00 Implementing the self-refinement loop 1:05:57 MATH-500 evaluation results 1:07:35 Takeaways and next steps