Outcome-Based Reinforcement Learning (OBRL) focuses on developing strategies that can predict future outcomes based on current decisions and past experiences. This approach emphasizes the importance of learning from both successes and failures to improve decision-making in dynamic environments. The user comments highlight a critical view on the potential risks associated with AI systems that optimize for specific outcomes, referencing the infamous 'paperclip maximizer' scenario as a cautionary tale. Additionally, some commenters suggest that simplifying environments might assist in enhancing predictive capabilities but might also reduce the robustness of the AI in more complex scenarios. This reflects a key dilemma in AI research: balancing predictability with adaptability.