Performance Gap Between Lion and AdamW in MiniLM Cross-Encoder Fine-Tuning on TREC DL 2020

Abstract

Abstract: Modern information retrieval systems often employ a two-stage pipeline: an efficient initial retrieval stage followed by a computationally intensive reranking stage. Cross-encoders have shown strong effectiveness for reranking due to their deep analysis of query-document pairs. This paper studies the impact of the Lion optimizer, a recent alternative to AdamW, during fine-tuning of cross-encoder rerankers. We fine-tune three transformer models-MiniLM, GTE, and ModernBERT-on the MS MARCO passage ranking dataset using both optimizers. GTE and ModernBERT support extended context lengths (up to 81

Research Question

How does the performance gap between Lion and AdamW scale with model size when fine-tuning MiniLM-Large vs. MiniLM-Base cross-encoders on the TREC DL 2020 dataset?

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