TAPS adapts recurrent step size to the trajectory, lifting Sudoku accuracy to 91.39% and Maze to 79.90% while cutting the loops needed for matched quality
The work introduces the Trajectory Adaptive Progress–Fluctuation Scheduler (TAPS), which uses exponential moving averages to estimate persistent progress and centered fluctuation in recurrent updates and adapts the per-step update scale online; it proves that under stated conditions TAPS reduces expected terminal loss and reaches a target quality in fewer loops, and empirically improves terminal accuracy with up to roughly 1.5x wall-clock speedup across Sudoku, Maze, language-model recurrence, and intermediate-layer recurrence.