The probability I give for scheming in the report is specifically for (goal-directed) models that are trained on diverse, long-horizon tasks (see also Cotra on “human feedback on diverse tasks,” which is the sort of training she’s focused on). I agree that various of the arguments for scheming could in principle apply to pure pre-training as well, and that folks (like myself) who are more worried about scheming in other contexts (e.g., RL on diverse, long-horizon tasks) have to explain what makes those contexts different. But I think there are various plausible answers here related to e.g. the goal-directedness, situational-awareness, and horizon-of-optimization of the models in questions (see e.g. here for some discussion, in the report, for why goal-directed models trained on longer episode seem more likely to scheme; and see here for discussion of why situational awareness seems especially likely/useful in models performing real-world tasks for you).
Re: “goal optimization is a good way to minimize loss in general”—this isn’t a “step” in the arguments for scheming I discuss. Rather, as I explain in the intro to report, the arguments I discuss condition on the models in question being goal-directed (not an innocuous assumptions, I think—but one I explain and argue for in section 3 of my power-seeking report, and which I think important to separate from questions about whether to expect goal-directed models to be schemers), and then focus on whether the goals in question will be schemer-like.
The probability I give for scheming in the report is specifically for (goal-directed) models that are trained on diverse, long-horizon tasks (see also Cotra on “human feedback on diverse tasks,” which is the sort of training she’s focused on). I agree that various of the arguments for scheming could in principle apply to pure pre-training as well, and that folks (like myself) who are more worried about scheming in other contexts (e.g., RL on diverse, long-horizon tasks) have to explain what makes those contexts different. But I think there are various plausible answers here related to e.g. the goal-directedness, situational-awareness, and horizon-of-optimization of the models in questions (see e.g. here for some discussion, in the report, for why goal-directed models trained on longer episode seem more likely to scheme; and see here for discussion of why situational awareness seems especially likely/useful in models performing real-world tasks for you).
Re: “goal optimization is a good way to minimize loss in general”—this isn’t a “step” in the arguments for scheming I discuss. Rather, as I explain in the intro to report, the arguments I discuss condition on the models in question being goal-directed (not an innocuous assumptions, I think—but one I explain and argue for in section 3 of my power-seeking report, and which I think important to separate from questions about whether to expect goal-directed models to be schemers), and then focus on whether the goals in question will be schemer-like.