Perplexity's JOLT Study Estimates 6.5-Fold Lower Training Token Use

Perplexity and NVIDIA researchers estimate JOLT would need 6.5 times fewer processed training tokens than Group Relative Policy Optimization (GRPO) to reach the same math benchmark score, indicating a possible efficiency gain. The comparison used Qwen3-0.6B and targeted 63.44% accuracy on MATH. GRPO reached that score at an observed checkpoint after 8.40 million processed tokens. JOLT's corresponding budget of 1.30 million tokens was inferred from a fitted accuracy curve. That makes the 6.5-fold difference an estimate at one accuracy target. It does not show that a JOLT training run takes 6.5 times less time or costs 6.5 times less money.