n8n学习外部调用

Previous work has heavily relied on large amounts of supervised data to enhance model
performance. In this study, we demonstrate that reasoning capabilities can be significantly
improved through large-scale reinforcement learning (RL), even without using supervised
fine-tuning (SFT) as a cold start. Furthermore, performance can be further enhanced with
the inclusion of a small amount of cold-start data. In the following sections, we present: (1)
DeepSeek-R1-Zero, which applies RL directly to the base model without any SFT data, and
(2) DeepSeek-R1, which applies RL starting from a checkpoint fine-tuned with thousands of
long Chain-of-Thought (CoT