Small-model reasoning distillation drops the fixed question set: 1,000 trajectories lift a 4B model from 62.9 to 75.3 on IMO-AnswerBench
Long-chain reasoning distillation no longer fixes its training questions up front: BoundaryEvo repeatedly has the student and a fixed expert answer the same candidates, keeping only questions the expert solves reliably and the student has not mastered, and with 1,000 training examples raises Qwen3.5-4B accuracy on IMO-AnswerBench from 62.9 to 75.3.