This scientific paper investigates the challenges of training analog physical systems for machine learning tasks due to inherent component imperfections and variability. Unlike precise digital networks, analog systems like the Contrastive Local Learning Network (CLLN), composed of self-adjusting resistors, exhibit limit cycles and scaling behaviors not seen in ideal models. Researchers developed an analytical model incorporating bias to explain these phenomena, finding that imperfections contribute to representational drift akin to that in biological brains. A novel, system-agnostic training method called "overclamping" is introduced and demonstrated to significantly suppress these negative effects, offering a promising path for robust analog learning by embracing, rather than eliminating, the messiness of real systems. The study highlights potential parallels between these physical learning systems and biological neural networks and suggests avenues for future research, particularly regarding the impact of noise and the potential for beneficial bias.