Lion Optimizer Convergence Stability in GTE Models for Low-Resource Passage Ranking versus AdamW

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the ad

Research goal: How does the Lion optimizer impact the convergence stability of GTE models when fine-tuned on low-resource passage ranking datasets like TREC-DL compared to AdamW?

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