Unverified50% confidenceFactExact time
Super introduces multi-teacher on-policy distillation (MOPD), transferring the base model's capabilities to agentic behavior.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/30/2026
First Seen
Valid until: 1/28/2027
Sources
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Related Claims
UnverifiedIn the reinforcement learning phase, Motif follows the general RL strategy from the Nemotron Ultra technical report: training multiple teacher models, then distilling all teachers using MOPD (Multi-Objective Policy Distillation).73% similarVerifiedOnline policy distillation (OPD) has the student model generate its own training responses, which are then supervised by the teacher model.70% similarUnverifiedSuper Mario reinforcement learning environments are typically based on the gym-super-mario-bros library, and input processing commonly uses techniques such as grayscaling, stacking 4 consecutive frames, and downsampling to 84×84 pixels.63% similarUnverifiedDINOv2的核心训练策略基于自蒸馏，模型同时维护教师网络和学生网络，学生网络通过预测教师网络对同一图像不同裁剪视角的输出来学习特征表示61% similar
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