Lithium ortho-thiophosphate (Li 3 PS 4 ) has emerged as a promising candidate for solid-state electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of Li-ion transport in Li 3 PS 4 are far from being fully understood, the role of PS 4 dynamics in charge transport still being controversial. In this work, we build machine learning potentials targeting state-of-the-art DFT references (PBEsol, r 2 SCAN, and PBE0) to tackle this problem in all known phases of Li 3 PS 4 (α, β, and γ), for large system sizes and time scales. We discuss the physical origin of the observed superionic behavior of Li 3 PS 4 : the activation of PS 4 flipping drives a structural transition to a highly conductive phase, characterized by an increase in Li-site availability and by a drastic reduction in the activation energy of Li-ion diffusion. We also rule out any paddle-wheel effects of PS 4 tetrahedra in the superionic phases�previously claimed to enhance Li-ion diffusion�due to the orders-of-magnitude difference between the rate of PS 4 flips and Li-ion hops at all temperatures below melting. We finally elucidate the role of interionic dynamical correlations in charge transport, by highlighting the failure of the Nernst−Einstein approximation to estimate the electrical conductivity. Our results show a strong dependence on the target DFT reference, with PBE0 yielding the best quantitative agreement with experimental measurements not only for the electronic band gap but also for the electrical conductivity of βand α-Li 3 PS 4 .