MathyAIwithMike

This episode explores a novel approach to fine-tuning large language models (LLMs) for better explanations using encoder-only transformers for semantic reward modeling. It addresses the drawbacks of traditional methods like 'LLM-as-judge' and keyword-based metrics. The solution involves a smaller encoder model that operates in the latent space of text embeddings, using cosine similarity to reward semantic alignment with expert explanations. This is implemented within the GRPO framework, incorporating a multi-faceted reward function that incentivizes factual accuracy, structural integrity, and transparency of reasoning, leading to higher-quality and more trustworthy LLM explanations.