The increasing computational demands of modern ML create a critical challenge: thorough experimentation becomes prohibitively expensive precisely when we most need to understand and steer model behavior. Small-scale experiments offer a powerful approach for systematic investigation, enabling both scientific understanding and practical advances. Recent work demonstrates the endless opportunities at this scale, including: diagnoses and mitigations of training pathologies, minimalistic replications of modern pipelines, elementary synthetic tasks that "stress test" architectures and motivate new designs and discovery of intriguing phenomena. This workshop highlights how methods and opportunities at small scale can unlock new insights and drive progress.