Variational quantum algorithms are being explored as a promising approach to finding useful applications for noisy intermediate-scale quantum computers. However, cost functions corresponding to many problems of interest are inherently global, defined by Hamiltonians with many-body interactions. Consequently, the optimization landscape can exhibit exponentially vanishing gradients, so-called barren plateaus, rendering optimal solutions difficult to find. Strategies for mitigating barren plateaus are therefore needed to make variational quantum algorithms trainable and capable of running on larger-scale quantum devices. In this work, we contribute the toolbox of perturbative gadgets to the portfolio of methods being explored in the quest for making noisy intermediate-scale quantum devices useful. Specifically, we introduce a novel perturbative gadget, tailored to variational quantum algorithms, that can be used to avoid barren plateaus. Our perturbative gadget encodes an arbitrary many-body Hamiltonian corresponding to a global cost function into the low-energy subspace of a three-body Hamiltonian. Our construction requires \(rk\) additional qubits for a \(k\)-body Hamiltonian comprising \(r\) terms. We provide rigorous guarantees on the optimization of the local cost function defined by our three-body gadget Hamiltonian with respect to the original cost function, and we prove that this local cost function exhibits non-vanishing gradients, thus delaying the onset of barren plateaus. We then provide numerical demonstrations to show the functioning of our approach.