TLDR: The article by Sebastian Raschka discusses reasoning models in large language models (LLMs), focusing on four approaches for enhancement: inference-time scaling, pure reinforcement learning (RL), supervised fine-tuning combined with RL, and model distillation. It distinguishes reasoning tasks that need intermediate steps, explains the development pipeline of DeepSeek's R1 models, and offers insights into efficient practices for building these models, even on a limited budget. Key takeaways include the emergence of reasoning through RL and the effectiveness of model distillation. Future trends involve combining RL with inference-time scaling for improved performance.
https://magazine.sebastianraschka.com/p/understanding-reasoning-llms