Four Approaches to Building Reasoning Models for LLMs
2025-02-06
This article explores four main approaches to enhancing Large Language Models (LLMs) with reasoning capabilities: inference-time scaling, pure reinforcement learning, supervised fine-tuning plus reinforcement learning, and model distillation. The development of DeepSeek R1 is used as a case study, showcasing how these methods can build powerful reasoning models, and how even budget-constrained researchers can achieve impressive results through distillation. The article also compares DeepSeek R1 to OpenAI's o1 and discusses strategies for building cost-effective reasoning models.