Recent advancements in motion forecasting have been significantly accelerated by self-supervised pre-training techniques. However, current methods typically rely on extensive fine-tuning of pre-trained models for downstream tasks like trajectory prediction, a process that can be inefficient. This inefficiency primarily arises because traditional fine-tuning strategies fail to fully exploit the close alignment between motion prediction tasks and masked reconstruction objectives used during pre-training. To overcome these limitations, we introduce Forecast-PEFT, a parameter-efficient fine-tuning framework specifically designed for motion forecasting. Forecast-PEFT preserves most of the pre-trained model's parameters, introducing minimal yet effective adjustments through novel prompts and adapters. By doing so, Forecast-PEFT significantly reduces the number of parameters needing retraining while preserving essential pre-trained knowledge. Our method demonstrates robust generalization across multiple datasets, achieving higher accuracy with only about 17% of the parameters required by traditional full fine-tuning approaches. Additionally, our fully fine-tuned variant, Forecast-FT, further enhances prediction performance, achieving improvements of up to 9.6% over conventional baseline methods. These findings highlight Forecast-PEFT as a scalable, efficient, and effective alternative for fine-tuning pre-trained motion forecasting models. Code is available at https://github.com/csjfwang/Forecast-PEFT.

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