Understanding Reasoning LLMs

The article by Sebastian Raschka, PhD, delves into the methodologies for enhancing reasoning capabilities within large language models (LLMs). It explains the four primary strategies for developing reasoning LLMs: inference-time scaling, pure reinforcement learning, supervised fine-tuning combined with reinforcement learning, and model distillation. Various cases like DeepSeek R1 are explored, highlighting how reasoning can emerge through reinforcement learning and the importance of supervised fine-tuning for more efficient and capable models.

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