The rapid growth of pre-trained models has made selecting suitable models for downstream tasks challenging. Exhaustively fine-tuning or evaluating every candidate model is computa- tionally expensive, while existing proxy-based metrics such as LEEP, NCE, and LogME pro- duce one-shot static rankings that do not use feedback from earlier evaluations. We propose Reinforcement Learning–based Model Rank- ing (RL-MR), a budget-aware framework that reformulates pretrained model selection as a sequential decision-making problem. RL-MR learns a ranking policy that uses proxy-based transferability signals as rewards to guide the order in which candidate models are evaluated. In controlled experiments on SST-2, Yelp Po- larity, Amazon Polarity, and DynaSent, RL- MR consistently improves ranking correlation over static proxy baselines, achieving up to Kendall’s τ = 0.8695 while maintaining Top-1 selection accuracy. These results suggest that sequential proxy feedback can provide a use- ful alternative to one-shot model ranking under limited evaluation budgets.