[HUGGINGFACE]score: 0.42
JAMEL Jointly Trains Agent Memory and Exploration via Novelty Signals
May 31, 2026
JAMEL co-trains a compressed latent memory and an exploration policy using novelty-driven interaction as a supervisory signal, avoiding the need for hand-labeled trajectories. The mutual dependency between memory and exploration is exploited so each improves the other during training in open-ended environments.
paper
HOW THIS AFFECTS YOU
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researcherWorth watching as a training-free labeling alternative for latent memory in long-horizon agentic settings, where step-level supervision is typically unavailable.