Artificial intelligence is revolutionizing oncology by transforming how malignancies are diagnosed, cancer biology is understood, and therapeutics are discovered. A cornerstone of this progress has been the availability of extensive, carefully curated datasets. Similarly, advances in AI-guided therapeutic strategies via Reinforcement Learning (RL) hinge upon carefully designed computational training environments that are both efficient and sufficiently realistic to capture key dynamics of cancer growth and therapy response. However, designing suitable models remains challenging for solid tumors, where emergent physical phenomena significantly influence therapeutic outcomes. Here, we introduce Reinforcement Failing, an AI-guided scientific discovery framework designed to reveal emergent physical mechanisms in tumor therapy. Applying this approach to adaptive therapy in solid tumors, we identify the pivotal roles of position-dependent proliferation and mechanically driven collective motion of resistant cells. Our findings highlight how integrating tumor physics into therapeutic strategies can optimize outcomes while mitigating hidden pitfalls during translation. Together, these results demonstrate that Reinforcement Failing is a powerful artificial scientific discovery engine for deciphering high-complexity processes in personalized cancer treatment and beyond.