Multi-Task Optimization over Networks of Tasks

arXiv cs.AI / 4/27/2026

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Key Points

The paper introduces MONET, a multi-task optimization algorithm that represents the task space as a graph where tasks are nodes connected by edges in parameter space.
MONET aims to overcome limitations of existing population-based methods by enabling scalable optimization for very large task sets (thousands of tasks) without relying on a fixed discretized archive that ignores task topology.
The method combines social learning (candidate generation via crossover from neighboring tasks) with individual learning (independent refinement of each task’s solution via mutation).
Experiments across four benchmark domains—including archery, arm, and cartpole (5,000 tasks each) and hexapod (2,000 tasks)—show MONET matches or outperforms MAP-Elites-based baselines in all tested domains.

Abstract

Multi-task optimization is a powerful approach for solving a large number of tasks in parallel. However, existing algorithms face distinct limitations: Population-based methods scale poorly and remain underexplored for large task sets. Approaches that do scale beyond a thousand tasks are mostly MAP-Elites variants and rely on a fixed, discretized archive that disregards the topology of the task space. We introduce MONET (Multi-Task Optimization over Networks of Tasks), a multi-task optimization algorithm that models the task space as a graph: tasks are nodes, and edges connect tasks in the task parameter space. This representation enables knowledge transfer between tasks and remains tractable for high-dimensional problems while exploiting the topology of the task space. MONET combines social learning, which generates candidates from neighboring nodes via crossover, with individual learning, which refines a node's own solution independently via mutation. We evaluate MONET on four domains (archery, arm, and cartpole with 5,000 tasks each; hexapod with 2,000 tasks) and show that it matches or exceeds the performance of existing MAP-Elites-based baselines across all four domains.