By Diego Almada Lopez
Publication Date: 2026-10-21 18:48:00
Training a large language model to reason well is expensive. Perplexity Research thinks it has found a way to make that process considerably cheaper.
The company, working with NVIDIA, introduced a method called JOLT, short for Joint On-Policy Learning and Teaching, on October 21, 2026. On the MATH benchmark, JOLT reached the same accuracy as Group Relative Policy Optimization (GRPO) while using 6.5 times fewer training tokens.
That matters because GRPO is already considered one of the more efficient options in the field.
One model, two jobs
A single model plays two roles at once: it teaches, and it learns.
In JOLT, the teacher role gets access to what the researchers call privileged information, such as verified solutions. It uses that extra context to give the student role specific, actionable feedback. The student absorbs that guidance as it trains.
The technical name for this is dense on-policy distillation. “On-policy” means the model learns from its own attempts rather than someone else’s, and “dense” means feedback arrives throughout the work instead of only at the end.
The method combines two ingredients: self-distillation, where a model learns from a better-informed version of itself, and reward-consistent teacher training. The second piece keeps the teacher’s guidance aligned with what the training process is actually trying to reward.
The research paper behind the work carries the title “A Good Self-Teacher Meets the…
