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arXiv Machine Learning By Jarod Ketcha Kouakep, Sreyvi UANN, Timoteo Carletti

Multi-Agent Learning with Cooperation-Driven Optimization Dynamics

Read the original on arXiv Machine Learning →

The paper proposes a cooperation mechanism for multiple small neural network agents that share predictions during training to reduce model complexity while maintaining performance. By incorporating shared predictions into the loss function, agents influence each other's weight updates through strategies such as voter, majority, and weighted average models. Experiments on standard benchmarks show that several small agents can outperform a single large model, achieving comparable accuracy with fewer parameters and lower computational cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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