The DeepSeek-R1 research paper has garnered attention for its innovative approach to training large language models (LLMs) using pure reinforcement learning (RL) techniques. According to experts, the paper emphasizes that reasoning capabilities can emerge from the model's interactions with its environment, rather than being pre-programmed. Key aspects of the training methodology include the use of Group Relative Policy Optimization (GRPO) and a multi-stage training approach. The research indicates that while RL is a powerful tool for enhancing reasoning capabilities, it is not the sole method required, as DeepSeek-R1 employs a combination of RL and supervised fine-tuning (SFT). The paper suggests that the compute costs associated with pure RL are high, but the potential for emergent behaviors and sophisticated reasoning makes it a compelling area of study for AI development.