Introduction
In recent years, Large Language Models (LLMs) have rapidly evolved, steadily narrowing the gap with Artificial General Intelligence (AGI). Post-training techniques have become an essential component of the complete training pipeline, enhancing model performance in reasoning tasks, social value alignment, and user preference adaptation, while requiring fewer computational resources compared to pre-training.
OpenAI’s o1 series models pioneered inference-time scaling by increasing the length of thought chains to enhance reasoning capabilities. This approach has shown significant improvements in mathematics, coding, and scientific reasoning tasks. However, the challenge of effectively scaling test-time computation remains an open question.
DeepSeek-R1 Paper Summary
This paper introduces DeepSeek-R1, a model trained through reinforcement learning that demonstrates exceptional reasoning capabilities…