Reinforcement learning has long been framed around the explore/exploit trade-off: an agent must gather information while also using what it already knows. A new preprint on arXiv (2610.25113) argues that this trade-off can be solved collectively by a minimal foraging swarm. The authors show that the swarm approaches the optimum of a multi-agent resource-sharing game, described as the ideal-free / Nash allocation.
Rather than relying on a central controller, the swarm's behavior emerges from local interactions. The paper connects this to self-organizing reinforcement learning, where exploration and exploitation are balanced at the group level. The abstract does not include experimental details or comparisons with other RL methods, so the claims should be read as a theoretical/conceptual result from the preprint.
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