Which statement about chain-of-thought strategies and learning from verified rewards in AI is accurate?

Which statement about chain-of-thought strategies and learning from verified rewards in AI is accurate?

• A. They have proven largely unsuccessful.
• B. They are not sufficient on their own; large-scale pretraining remains central.
• C. They are the sole determinant of modern AI progress.
• D. They render pretraining unnecessary.

Answer: (A)

Chain-of-thought prompting and learning from verified rewards help models reason more effectively and align with human preferences, but they don’t replace the need for broad knowledge learned during large-scale pretraining. In practice, these strategies provide meaningful gains on specific tasks or in shaping behavior, yet the overall performance and capabilities of modern AI still hinge largely on training on massive, diverse data. So they aren’t sufficient on their own; large-scale pretraining remains central to progress. The other statements misrepresent the evidence: these approaches have shown benefits, they do not render pretraining unnecessary, and they are not the sole determinant of progress.

Chain-of-thought prompting and learning from verified rewards help models reason more effectively and align with human preferences, but they don’t replace the need for broad knowledge learned during large-scale pretraining. In practice, these strategies provide meaningful gains on specific tasks or in shaping behavior, yet the overall performance and capabilities of modern AI still hinge largely on training on massive, diverse data. So they aren’t sufficient on their own; large-scale pretraining remains central to progress. The other statements misrepresent the evidence: these approaches have shown benefits, they do not render pretraining unnecessary, and they are not the sole determinant of progress.