GRPO curricula stop following a fixed schedule: probe accuracy steers difficulty sampling, with the largest gains at intermediate levels
In GRPO reinforcement fine-tuning, a fixed easy-to-hard curriculum can drift from the model's actual progress. ClosedLoopGaussian keeps the Gaussian sampler's form but uses periodic probe accuracy to adjust how fast training advances and how widely it rehearses, leaving the reward, architecture and optimizer unchanged. On Countdown, mean exact-match accuracy over four seeds exceeds both the fixed curriculum and vanilla GRPO, with gains concentrated at intermediate difficulty, though seed dispersion is large and the comparison is read descriptively.