AIBullisharXiv – CS AI · 6h ago7/10
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Joint Agent Memory and Exploration Learning via Novelty Signals
Researchers introduce JAMEL, a framework that trains AI agents to explore open-ended environments more effectively by jointly developing memory systems and exploration policies through novelty-driven learning. The approach uses natural supervisory signals like code coverage to train compressed memory representations, achieving exploration capabilities that rival closed-source models while reducing computational token consumption.