Part 4/11:
An essential aspect of moving forward is how to design diverse task distributions that AI models can robustly handle. For example, envisioning a grid environment where an agent must navigate from a start point to a goal requires considering variations in grid structures without prior knowledge of what the agent will face post-training. The challenge revolves around creating frameworks that minimize performance discrepancies—termed “regret”—between the agent's actions in any given environment and those of an optimal policy. Regret quantifies the difference between a model's performance and the best achievable outcome within that specific environment.