  Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model
to a target domain using only unlabeled target data. Current SFDA methods face
challenges in effectively leveraging pre-trained knowledge and exploiting
target domain data. Multimodal Large Language Models (MLLMs) offer remarkable
capabilities in understanding visual and textual information, but their
applicability to SFDA poses challenges such as instruction-following failures,
intensive computational demands, and difficulties in performance measurement
prior to adaptation. To alleviate these issues, we propose Reliability-based
Curriculum Learning (RCL), a novel framework that integrates multiple MLLMs for
knowledge exploitation via pseudo-labeling in SFDA. Our framework incorporates
proposed Reliable Knowledge Transfer, Self-correcting and MLLM-guided Knowledge
Expansion, and Multi-hot Masking Refinement to progressively exploit unlabeled
data in the target domain. RCL achieves state-of-the-art (SOTA) performance on
multiple SFDA benchmarks, e.g., $\textbf{+9.4%}$ on DomainNet, demonstrating
its effectiveness in enhancing adaptability and robustness without requiring
access to source data. Code: https://github.com/Dong-Jie-Chen/RCL.
