AI RESEARCH
On the Construction and Implications of Low-Loss Valleys in LoRA-based Bayesian Inference
arXiv CS.LG
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ArXi:2605.29580v1 Announce Type: new While parameter-efficient fine-tuning methods like low-rank adaptation (LoRA) are standard for large language models, principled estimation of epistemic uncertainty remains challenging. Recent results in the LoRA regime suggest that discrete multi-mode approaches such as deep ensembles offer little benefit over single-mode methods.