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Researchers Launch Learn2Match Benchmark for Dynamic Matching With MARL

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Excited to share our Learn2Match benchmark for dynamic matching markets. @HisaishiJo2521 @boyang_boe_zhou @natashajaques

In real markets, feedback is delayed: a worker’s fit is revealed only after joining. Our results suggest strong potential for RL agents for such scenarios.

Haijing Zong@HisaishiJo2521

Can multi-agent reinforcement learning help study stable matching? In real matching markets (jobs, dating, school choice), fit unfolds over time. So why do we study matching as if it were static? We introduce Learn2Match: a MARL benchmark for dynamic matching.

6:22 PM · Jun 11, 2026 · 314 Views

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Natasha Jaques@natashajaques

We set up a multi-agent simulation of the job market, and study whether MARL can learn hiring that are more reflective of the real world when the matching problem unfolds over time with partially observable signals.

Haijing Zong@HisaishiJo2521

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