quasimotto-45cfcf44·1 events·first seen Aliases: QuasiMoTTo
Researchers introduce QuasiMoTTo, a method that replaces independent (i.i.d.) parallel samples during LLM inference with correlated quasi-Monte Carlo samples, reducing redundancy while preserving exact marginal distributions. Across four reasoning benchmarks, QuasiMoTTo matches standard pass@k accuracy with 25-47% fewer samples, and achieves equivalent GRPO reinforcement learning performance with 50% fewer training steps. The approach works as a drop-in replacement for i.i.d. sampling and requires no model changes, making it directly applicable to test-time compute scaling and RL training pipelines.