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Model Training

Understanding Reasoning LLMs

A comprehensive exploration of reasoning LLMs focuses on four main approaches: inference-time scaling, pure reinforcement learning, supervised finetuning with RL, and pure supervised finetuning with distillation. The article analyzes DeepSeek R1's development pipeline and compares it with OpenAI's o1, highlighting how reasoning capabilities can emerge through different training methodologies. Practical insights are provided for developing reasoning models on limited budgets, including alternative approaches like journey learning and small-scale implementations.

S1: The $6 R1 Competitor?

A groundbreaking paper demonstrates how a $6 AI model, running on a laptop, achieves near state-of-the-art performance using only 1,000 training examples and innovative inference-time scaling techniques. The research reveals simple yet effective methods for controlling AI model thinking time and highlights the accelerating pace of AI development through cost-effective experimentation.