Chain-of-Thought Reasoning in Large Language Models - Exploring SFT and RL Relevance

Explore a technical analysis of OpenAI's o3 model's Chain-of-Thought (CoT) reasoning mechanisms during inference time in this 27-minute research presentation. Dive deep into the relationship between Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and test-time reasoning capabilities. Learn how language models are taught to explicitly reason over safety specifications through CoT, breaking down complex problems into intermediate steps rather than relying solely on pattern recognition. Examine the innovative Alignment Fine-Tuning (AFT) paradigm that addresses assessment misalignment issues through a three-step process involving COT training, response generation, and score calibration. Understand the implications of explicit reasoning versus implicit pattern learning in language models, drawing from research by HuggingFace and OpenAI on scaling test-time compute and deliberative alignment strategies.