arXiv:2605.26184v1 Announce Type: cross
Abstract: Hybrid post-training usually combines supervised fine-tuning and reinforcement learning, but fixed mixing schedules cannot adapt when the relative noise of the two signals changes over time. We propose GAC, a noise-aware controller that derives an adaptive mixing weight from online estimates of gradient variance and disagreement between the two training signals. The method adds smoothing, prior guidance, and bounded updates while reusing existing training tensors. Experiments on math, code, science, and logic benchmarks show that GAC consistently improves hybrid post-training over strong fixed and rule-based baselines, with larger gains at larger model scales and less than 1% training overhead.
Portable automated rapid testing for auditory assessment: repeated at-home testing in older adults
IntroductionHearing challenges are prevalent in older adults and are associated with age-related cognitive decline. However, measuring age-related changes in hearing faces critical barriers related to