How controllers from industrial machinery can coordinate multitask machine learning

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Machine learning

How controllers from industrial machinery can coordinate multitask machine learning

Instead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically.

By Theodore Vasiloudis

July 30, 2026

8 min read

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Key takeaways

ControlG uses industrial control system principles — specifically proportional-integral-derivative (PID) controllers — to coordinate multiple conflicting training objectives in graph machine learning by allocating computational capacity sequentially rather than blending gradients at each step. The framework operates across three time scales: estimating per-objective difficulty through spectral-demand and interference metrics, optimizing per-epoch computation allocation using log-hypervolume sensitivity, and tracking the allocation plan via PID feedback loops. ControlG eliminates three common multitask learning failures — disagreement (negative transfer), drift (changing objective relevance), and drought (objectives starved to zero weight) — by temporally separating objectives instead of forcing per-step compromise.

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Training a machine learning model to handle multiple objectives simultaneously is a bit like trying to follow GPS directions to several destinations at once: the routes often conflict, and compromising between them can leave you farther from every destination. A paper we presented at this year’s International Conference on Machine Learning ( ICML ) addresses this problem in the context of graph self-supervised learning (graph SSL), where the goal is to train a neural network to process graph data. Graph SSL objectives include inferring links between graph nodes, reconstructing nodes that have been masked out, and maximizing the mutual information between a given node and the nodes in its neighborhood. Our framework, ControlG, borrows an idea from industrial control systems: rather than blending all objectives together at every training step, it dedicates computational capacity to one objective at a time and lets a proportional-integral-derivative (PID) controller decide which objective needs attention next. Karish Grover, an Amazon PhD fellow, performed this work during an internship at Amazon Web Services, and Amazon Scholar Christos Faloutsos and I served as his mentors.

The key insight behind ControlG. Per-step mixing (left) forces compromise when objectives conflict. ControlG separates objectives in time (center) , dedicating a separate computational block to each. The learned schedule (right) is interpretable: early training explores all objectives (link prediction, mutual information, reconstruction, and contrastive learning ); mid-training prioritizes mutual information after determining that another objective has been interfering with it; and late training focuses on the reconstruction objective, which has been lagging.

The problem: Multitask tug-of-war

The...