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This article discusses a research paper showing that the disagreement rate between two deep networks trained with different random seeds can accurately estimate generalization error using only unlabeled data, revealing a surprising connection called Generalization Disagreement Equality.
Proposes a labeled-data-free meta-learning method that generates tasks by assigning soft labels from pre-trained models to unlabeled data, avoiding computationally expensive model inversion. Achieves up to 104x speedup and 8.4-36.4% accuracy improvements over state-of-the-art DFML methods.
Introduces Test-Time Reinforcement Learning (TTRL), a method that uses majority voting on unlabeled data to create pseudo-labels for RL training, enabling self-improvement of LLMs without ground-truth answers. Achieves significant gains (e.g., +159-211% on AIME 2024 for Qwen-2.5-Math-7B).
This paper investigates disagreement-based drift detection in ensembles of incremental decision trees, finding that while effective in neural networks, the method underperforms loss-based detectors for tree ensembles due to limited model plasticity.