JOLT trains the teacher too, with penalties when its predictions diverge from the student
perplexity_ai · x · 2026-10-22
Perplexity explains JOLT's design: in standard self-distillation only the student trains, so the teacher can exploit shortcuts via privileged information. JOLT instead also trains the teacher on outcome rewards from its own attempts, adding a penalty when its predictions diverge from the student's — ensuring the feedback is usable by the student.
Related event: JOLT explained: dual teacher-student roles beat GRPO across five benchmarks(2 posts)→
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