SIREN-RoPE learns the rotation space in RoPE from timestamps, cyclical patterns, and categorical signals using SIREN, improving calibration and ranking in a production news recommender with negligible overhead.
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HDET lets data-parallel replicas explore a spread of learning rates independently before averaging parameters, with an auto-LR controller driven by inter-replica loss differences to produce a self-adapting schedule without extra sweeps.
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Learning to Rotate: Temporal and Semantic Rotary Encoding for Sequential Modeling
SIREN-RoPE learns the rotation space in RoPE from timestamps, cyclical patterns, and categorical signals using SIREN, improving calibration and ranking in a production news recommender with negligible overhead.
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Scalable Hyperparameter-Divergent Ensemble Training with Automatic Learning Rate Exploration for Large Models
HDET lets data-parallel replicas explore a spread of learning rates independently before averaging parameters, with an auto-LR controller driven by inter-replica loss differences to produce a self-adapting schedule without extra sweeps.