Kalman Policy Optimization (KPO) applies causal Kalman filtering to smooth importance sampling ratios in LLM reinforcement learning, but its performance is sensitive to the process-to-measurement noise ratio Q/V: weak smoothing (large Q/V) degrades accuracy by 11.79 percentage points on MATH-500. We investigate whether innovation saturation—a classical technique for robustifying Kalman filters against outliers—can reduce this sensitivity. Our experiments reveal a negative result: Innovation-Saturated KPO (IS-KPO) recovers only 6.6% of the performance gap, with the improvement not statistically significant (p ≈ 0.16). Diagnostic analysis shows the saturation mechanism almost never activates (clip fraction < 10−6) because KPO’s measurement noise V = 1.0 creates a clipping threshold far larger than actual innovations. Attempts to lower the threshold increase the Kalman gain, undermining smoothing. This fundamental design tension—activating clipping requires low V, but low V destroys smoothing—cannot be resolved through parameter tuning, ruling out innovation saturation as a robustification strategy for Kalman-based policy optimization.