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arXiv · 2507.12419

Mixture of Raytraced Experts

Andrea Perin·Giacomo Lagomarsini·Claudio Gallicchio·Giuseppe Nuti
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Abstract

We introduce a Mixture of Raytraced Experts, a stacked Mixture of Experts (MoE) architecture which can dynamically select sequences of experts, producing computational graphs of variable width and depth. Existing MoE architectures generally require a fixed amount of computation for a given sample. Our approach, in contrast, yields predictions with increasing accuracy as the computation cycles through the experts' sequence. We train our model by iteratively sampling from a set of candidate experts, unfolding the sequence akin to how Recurrent Neural Networks are trained. Our method does not require load-balancing mechanisms, and preliminary experiments show a reduction in training epochs of 10\% to 40\% with a comparable/higher accuracy. These results point to new research directions in the field of MoEs, allowing the design of potentially faster and more expressive models. The code is available at https://github.com/nutig/RayTracing

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Andrea Perin, Giacomo Lagomarsini, Claudio Gallicchio, Giuseppe Nuti. 2025-07-16. Mixture of Raytraced Experts. https://arxiv.org/abs/2507.12419

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