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Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning. However, existing agent harnesses typically either evolve their states only through forward information flow or rely on additional replay or validation data for harnes...
This work proposes a reinforcement learning (RL) fine-tuning framework that treats EHR foundation models as generative policies over patient trajectories, and finds that RL fine-tuning consistently improves over pre-trained backbones and strong baselines.
MatrAIx is introduced, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users and provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users.
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