You can make reasoning models faster and more accurate by verifying multi-step reasoning at the step level using only the model's internal signals, avoiding the overhead of external reward models.
SpecGuard improves speculative decoding—a technique that speeds up AI model inference—by verifying each reasoning step before accepting it, rather than just checking individual tokens. It uses internal model signals like attention patterns and confidence scores to catch errors early, improving both accuracy and speed without needing external reward models.