After a year, DeepSeek hasn’t updated R2, but they quietly updated the paper for R1 (V2). Here are a few key points: 1. Regarding the phenomenon of LLM responses mentioning OpenAI/ChatGPT, they provided an explanation: it’s due to the objective presence of externally generated content in web data, which indirectly influenced the base model during training. 2. They explicitly listed 'structured output' and 'tool usage' as key focuses for future evolution (R2?). (These are also the cornerstones for building Agentic systems.) 3. Added 'token efficiency' as a clear direction for future optimization, aiming to reduce overthinking on simple problems. (GPT-5.1 also mentioned a similar goal.) 4. In terms of narrative, DeepSeek not only demonstrated the effectiveness of pure reinforcement learning with minimal human intervention but also attempted to establish an 'incentive-driven' academic paradigm. This shift sends a clear signal to the industry: instead of relying on large-scale human annotations, guiding models toward 'self-realization' through proper incentive design is the ultimate path to general reasoning. https://arxiv.org/abs/2501.12948v2