llm-training
A new distillation method lets the teacher model interrupt the student mid-thought News
Researchers found that when a student model starts reasoning down a wrong path, its teacher's next word tends to be a redirection like But or Wait, and turned that disagreement into an automatic trigger for the teacher to briefly take over.
A weaker model just taught a stronger one, by passing on the lesson instead of the answers News
Researchers from Tsinghua and ByteDance show that a small model's reinforcement learning gains can be distilled into a larger, already-stronger model by transferring the change in the teacher's policy rather than its outputs, letting a 1.5B teacher improve a 7B student.
Two new papers push 'on-policy distillation' to fix privileged teachers and merge specialist skills News
DOPD and MOPD advance on-policy distillation -- training a student on its own outputs -- with DOPD routing supervision to avoid a 'privilege illusion' and MOPD merging multiple specialist RL teachers into one model without cross-domain interference.