Unverified50% confidenceFactExact time
Mainstream imitation learning methods are built on the assumption of learning only from perfect reasoning trajectories, such that the model never sees how to adjust midway through an error.
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9/18/2026
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Related Claims
UnverifiedSupervised fine-tuning trains models to imitate correct answers labeled by humans, while reinforcement learning fine-tuning allows models to learn optimal strategies through trial and error.71% similarUnverifiedHaving an AI model review its own output tends only to repeat its initial assumptions, because the errors and the verification logic originate from the same training distribution, knowledge structure, and reasoning preferences.69% similarUnverifiedModel Routing and Hybrid Inference have already been explored in academia and were not pioneered by Cognition.69% similarVerifiedResearch has found that the chain-of-thought reasoning generated by models does not fully correspond to their actual internal computation processes, and may provide post-hoc explanations unrelated to the true decision-making path.68% similarUnverifiedHallucinations stem from the model's optimization objective itself—the model is trained to generate linguistically coherent and statistically plausible continuations, not factually accurate answers.67% similar
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