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Finite State Machine (FSM) is the backbone of an important class of applications in many domains. Its parallelization has been extremely difficult due to inherent strong dependences in the computation. Recently, principled speculation shows good promise to solve the problem. However, the reliance on offline training makes the approach inconvenient to adopt and hard to apply to many practical FSM applications, which often deal with a large variety of inputs different from training inputs.This work presents an assembly of techniques that completely remove the needs for offline training. The techniques include a set of theoretical results on inherent properties of FSMs, and two newly designed dynamic optimizations for efficient FSM characterization. The new techniques, for the first time, make principle speculation applicable on the fly, and enables swift, automatic configuration of speculative parallelizations to best suit a given FSM and its current input. They eliminate the fundamental barrier for practical adoption of principle speculation for FSM parallelization. Experiments show that the new techniques give significantly higher speedups for some difficult FSM applications in the presence of input changes.