Robust Checkpoint Selection for Multimodal LLMs via Agentic Evaluation and Stability-Aware Ranking
The paper discusses the challenges of checkpoint selection for multimodal large language models (MLLMs) due to performance differentials and evaluation noise. It proposes a multi-stage framework that incorporates real-world data and various ranking protocols to improve evaluation reliability. The authors emphasize the importance of data quality, particularly in OCR readability, for valid evaluations.
- ▪Checkpoint selection for MLLMs faces challenges from marginal performance differentials and noisy evaluation signals.
- ▪The proposed framework integrates curated data and structured judgment to enhance evaluation reliability.
- ▪Data quality, especially OCR readability, is crucial for the validity of evaluations.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2605.18852 (cs) [Submitted on 13 May 2026] Title:Robust Checkpoint Selection for Multimodal LLMs via Agentic Evaluation and Stability-Aware Ranking Authors:Qinwu Xu, Zhuoheng Li, Jessie Salas View a PDF of the paper titled Robust Checkpoint Selection for Multimodal LLMs via Agentic Evaluation and Stability-Aware Ranking, by Qinwu Xu and 2 other authors View PDF HTML (experimental) Abstract:Checkpoint selection for multimodal large language models (MLLMs) presents significant challenges when performance differentials are marginal and evaluation signals are prone to noise.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
