Understanding Reasoning LLMs
Read OriginalThis article provides a detailed analysis of the four primary methods for developing reasoning models within LLMs: inference-time scaling, pure reinforcement learning, SFT+RL, and pure supervised fine-tuning. It defines reasoning models, discusses their use cases, and examines the trade-offs involved, using examples like the DeepSeek training pipeline to illustrate the concepts for AI and machine learning practitioners.
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