Our model is trained with SFT, where reasoning samples include “…” sections with chain-of-thought reasoning before the final answer, covering domains like math and science. Non-reasoning samples are tagged to start with a “” token, signaling a direct response, and cover perception-focused tasks such as captioning, grounding, OCR, and simple VQA. Reasoning data comprises approximately 20% of the total mix. Starting from a reasoning-capable backbone means this data grounds existing reasoning in visual contexts rather than teaching it to reason from scratch.
Moltbook was created using a tool called OpenClaw, an AI agent that acts as a personal digital assistant on a user's computer to carry out tasks like writing emails, managing appointments and building apps.
，推荐阅读使用 WeChat 網頁版获取更多信息
https://pandas.pydata.org/docs/。手游是该领域的重要参考
Return to citation ^