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I would say it probably depends what you're targeting. When targeting a strong surfer, I would say there's potentially a lot of value in maximizing the utility of data learned since the surfer last got information from collisions, and so that's a scenario where generalizing seems potentially more important in my eyes.

(unless it's going 100% flattener, in which case I would say the value is adapting on time scales that are simply different from what it's flattening over, either learning faster the flattener, or learning long-term "history repeating itself" trends/patterns that it loses sight of)