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.
两小时车程外，江苏苏州吴中区，绿的谐波传动科技股份有限公司副总经理李谦正摆弄着一台人形机器人的肘关节谐波减速器，研究如何让减速器性能更佳、重量更轻。
。新收录的资料是该领域的重要参考
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«Мое мнение однозначно: нам нужно двигаться дальше, нельзя останавливаться на достигнутом. Космос должен быть изучен, а продолжать его исследование лучше с Луны и ближних планет — Марса, Венеры», — сказала парламентарий.