UNDERSTANDING REASONING LLMS

Summary from the TLDR Web Dev newsletter

There are four main methods for building reasoning models, which are LLMs that perform well on multi-step tasks. These methods are inference-time scaling (increasing computational resources during inference), pure reinforcement learning (RL), supervised fine-tuning (SFT) combined with RL, and knowledge distillation via SFT of smaller models. DeepSeek R1 models are a case study on the effectiveness of SFT+RL. Distillation is great for cost-effective model development.