One LR Doesn’t Fit All: Heavy-Tail Guided Layerwise Learning Rates for LLMs

arXiv:2605.22297v3 Announce Type: replace-cross Abstract: Learning rate configuration is a fundamental aspect of modern deep learning. The prevailing practice of applying a uniform learning rate across all layers overlooks the structural heterogeneity of Transformers, potentially limiting their effectiveness as the backbone of Large Language Models (LLMs). In this paper, we introduce Layerwise Learning Rate (LLR), […]

SetupX: Can LLM Agents Learn from Past Failures in Functionality-Correct Code Repository Setup?

arXiv:2605.26186v2 Announce Type: cross Abstract: Functionality-correct repository setup aims to configure execution environments (e.g., dependencies, build scripts) to successfully execute a repository’s documented features. It presents significant challenges due to diverse, repository-specific failures, including dependency incompatibilities, missing toolchains, incomplete installations, and verification-strategy mismatches. Existing LLM agents struggle to robustly resolve these issues, specifically failing to […]

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