Current flagship processors excel at extracting instruction-level-parallelism (ILP) by forming large instruction windows. Even then, extracting ILP is inherently limited by true data dependencies. Value prediction was proposed to address this limitation. Many challenges face value prediction, in this work we focus on two of them. Challenge #1: store instructions change the values in memory, rendering the values in the value predictor stale, and resulting in value mispredictions and a retraining penalty. Challenge #2: value mispredictions trigger costly pipeline flushes. To minimize the number of pipeline flushes, value predictors employ stringent, yet necessary, high confidence requirements to guarantee high prediction accuracy. Such requirements can negatively impact training time and coverage.