Downstream Model

Downstream models are specialized models trained on top of pre-trained foundation models (like large language or vision transformers) to perform specific tasks. Current research focuses on improving the efficiency and robustness of this transfer learning process, exploring techniques like adaptive feature transfer, and addressing challenges such as adversarial attacks and reward overoptimization in reinforcement learning settings. This work is crucial for optimizing the performance and resource efficiency of AI systems across various applications, while also enhancing transparency and traceability in the model development lifecycle. The ultimate goal is to leverage the power of foundation models while mitigating their limitations and risks for practical deployment.