This approach is not without limitations. The balance between modes is a direct function of design choices we made, informed by recent literature (opens in new tab) and observed model behavior during training—though the boundary between modes can be imprecise as it is learned implicitly from the data distribution. Our model allows control through explicit prompting with “” or “” tokens when the user wants to override the default reasoning behavior. The 20/80 reasoning-to-non-reasoning data split may not be optimal for all domains or deployment contexts. Evaluating the ideal balance of data and the model’s ability to switch appropriately between modes remains an open problem.
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And we think that works with our brands. Based on the economics and the studios that we have, as I said, the first couple of games are going to cover a bunch of startup costs. So those might not be as profitable as we want, but we think over time, as we start to optimize those studios, figure out how we leverage great talent spots like Eastern Europe and markets like Montreal, where we have one of our biggest offices, we think we can get profitable at that over time and delight a lot of people in the process.
反之，理想汽车就在2025年吃到了速度慢的大亏。