  Recent progress in motion forecasting has been substantially driven by
self-supervised pre-training. However, adapting pre-trained models for specific
downstream tasks, especially motion prediction, through extensive fine-tuning
is often inefficient. This inefficiency arises because motion prediction
closely aligns with the masked pre-training tasks, and traditional full
fine-tuning methods fail to fully leverage this alignment. To address this, we
introduce Forecast-PEFT, a fine-tuning strategy that freezes the majority of
the model's parameters, focusing adjustments on newly introduced prompts and
adapters. This approach not only preserves the pre-learned representations but
also significantly reduces the number of parameters that need retraining,
thereby enhancing efficiency. This tailored strategy, supplemented by our
method's capability to efficiently adapt to different datasets, enhances model
efficiency and ensures robust performance across datasets without the need for
extensive retraining. Our experiments show that Forecast-PEFT outperforms
traditional full fine-tuning methods in motion prediction tasks, achieving
higher accuracy with only 17% of the trainable parameters typically required.
Moreover, our comprehensive adaptation, Forecast-FT, further improves
prediction performance, evidencing up to a 9.6% enhancement over conventional
baseline methods. Code will be available at
https://github.com/csjfwang/Forecast-PEFT.
