Inf-SSM constrains the infinite-horizon evolution of SSMs via Grassmannian geometry and an efficient O(n^2) Sylvester solver to enable exemplar-free continual learning with reduced forgetting.
Ishaan Watts, Catherine Li, Sachin Goyal, Jacob Mitchell Springer, and Aditi Raghunathan
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Learning rate decay during SFT increases pretrained model sharpness, which exacerbates catastrophic forgetting and causes overtraining in LLMs.
Proposes the CBDT framework as a minimum viable digital twin for CI builds to enable real-time monitoring, ML modeling, and prescriptive optimization of build duration, failures, and flakiness.
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Exemplar-Free Continual Learning for State Space Models
Inf-SSM constrains the infinite-horizon evolution of SSMs via Grassmannian geometry and an efficient O(n^2) Sylvester solver to enable exemplar-free continual learning with reduced forgetting.
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(How) Learning Rates Regulate Catastrophic Overtraining
Learning rate decay during SFT increases pretrained model sharpness, which exacerbates catastrophic forgetting and causes overtraining in LLMs.
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Towards Build Optimization Using Digital Twins
Proposes the CBDT framework as a minimum viable digital twin for CI builds to enable real-time monitoring, ML modeling, and prescriptive optimization of build duration, failures, and flakiness.