Unverified50% confidenceOpinionExact time
The current NTP+RL paradigm has three mechanistic gaps: the absence of goal generation, the absence of embodiment and interaction, and the absence of continual learning.
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50%
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Long-term
Relevance
10/12/2026
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UnverifiedThe shared limitation of Hebbian learning and competitive learning is the lack of an explicit optimization objective, making end-to-end fine-tuning for downstream tasks difficult, and they are typically used for feature extraction and representation learning.67% similarVerifiedThe core challenge of internalizing knowledge into model parameters is the Catastrophic Forgetting problem, which is a difficulty not yet fully resolved in the field of continual learning.64% similarUnverifiedThe core explanation for this phenomenon is task complementarity: when the new task does not conflict with existing knowledge and even mutually reinforces it, the premise on which continual learning depends does not hold, and forcibly imposing regularization constraints is counterproductive.64% similarUnverifiedOut-of-distribution generalization failure is an inherent limitation of the current supervised learning paradigm and cannot be fundamentally resolved by adding human review.64% similarUnverifiedExisting RL methods based on group-relative advantage have two weak links in the correction chain from reward to parameter update: advantage collapse during the rollout stage and gradient invisibility at the optimization level.64% similar
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