Spectral Alignment in Forward-Backward Representations via Temporal Abstraction
The paper investigates how temporal abstraction, specifically action repetition, acts as a low-pass filter to align the high-rank dynamics of continuous environments with the low-rank structure expected by Forward-Backward representations. By increasing action repetition (k), the representation becomes more structured and stable, leading to better long-horizon performance in continuous control.