Fine-tuned 7B LLMs generating unified diffs for neural architecture refinement achieve 66-75% valid rates and 64-66% mean first-epoch accuracy, outperforming full-generation baselines by large margins while cutting output length by 75-85%.
Gradient-based learning applied to document recog- nition.Proceedings of the IEEE, 86(11):2278–2324, 1998
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SubFLOT uses optimal transport to generate data-aware personalized submodels via server-side pruning and scaling-based adaptive regularization to mitigate parametric divergence in heterogeneous federated learning.
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Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs
Fine-tuned 7B LLMs generating unified diffs for neural architecture refinement achieve 66-75% valid rates and 64-66% mean first-epoch accuracy, outperforming full-generation baselines by large margins while cutting output length by 75-85%.
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SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
SubFLOT uses optimal transport to generate data-aware personalized submodels via server-side pruning and scaling-based adaptive regularization to mitigate parametric divergence in heterogeneous federated learning.