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arXiv:2602.15260v1 Announce Type: new Abstract: On-policy distillation (OPD), which samples trajectories from the student model and supervises them with a teacher at the token level, …
arXiv:2602.15283v1 Announce Type: new Abstract: Modern deep neural networks achieve high predictive accuracy but remain poorly calibrated: their confidence scores do not reliably reflect the …
arXiv:2602.15304v1 Announce Type: new Abstract: Collaborative clinical decision support is often constrained by governance and privacy rules that prevent pooling patient-level records across institutions. We …
arXiv:2602.15322v1 Announce Type: new Abstract: Training large language models (LLMs) relies almost exclusively on dense adaptive optimizers with increasingly sophisticated preconditioners. We challenge this by …
arXiv:2602.15330v1 Announce Type: new Abstract: The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale …
arXiv:2602.15332v1 Announce Type: new Abstract: Understanding how language models carry out long-horizon reasoning remains an open challenge. Existing interpretability methods often highlight tokens or spans …
arXiv:2602.15337v1 Announce Type: new Abstract: Asynchronous Federated Learning (AFL) has emerged as a significant research area in recent years. By not waiting for slower clients …
arXiv:2602.15344v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly augmented with long-term memory systems to overcome finite context windows and enable persistent reasoning …
arXiv:2602.15367v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved notable performance in high-dimensional sequential decision-making tasks, yet remains limited by low sample efficiency, sensitivity …
arXiv:2602.15380v1 Announce Type: new Abstract: Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, …
arXiv:2602.15393v1 Announce Type: new Abstract: Standard Mean-Shift algorithms are notoriously sensitive to the bandwidth hyperparameter, particularly in data-scarce regimes where fixed-scale density estimation leads to …
arXiv:2602.15405v1 Announce Type: new Abstract: Robust classification in noisy environments remains a fundamental challenge in machine learning. Standard approaches typically treat signal enhancement and classification …