We demonstrate deterministic, field-free magnetization reversal of a single-domain nanomagnet within 300 ps under a current density of $3\times {10}^{10}{\text{A/m}}^2$ by coupling reinforcement learning (RL) to the Landau-Lifshitz-Gilbert equation with the spin-orbit torques (SOTs). The RL agent autonomously discovers a current waveform that minimizes the magnetization trajectory path and exploits a precessional shortcut enabled by the fieldlike SOT and hard-axis anisotropy. From the learned pulse, we extract a clear physical picture of the dynamics and develop a model-based analytical framework that establishes a lower bound on the switching time. The control strategy remains robust across a wide range of damping constants and is stabilized against thermal fluctuations at higher current densities. We also discuss feasible experimental implementations for the precessional switching.

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• Spin-orbit torque
• Landau-Lifschitz-Gilbert equation
• Machine learning