Apple's paper on the limitations of Large Reasoning Models (LRMs) suggests these models struggle with complex reasoning similar to humans, indicating that scaling may not suffice for developing AGI.
The criticisms against Apple's paper include that LRMs can't solve problems due to output token limitations, but the paper's findings go beyond this issue.
Some criticisms focus on the authorship, noting an intern was involved, but the work's quality should be the primary concern, not the author's status.
Larger models sometimes perform better due to specific improvements but lack consistency, meaning we can't predict which model will solve which problem effectively.
Symbolic AI and the integration of neural networks with symbolic algorithms show promise, supporting claims that a combined approach may be needed for effective reasoning.
Despite Apple's paper having limited examples, it aligns with earlier research showing poor generalization by current AI models, highlighting weaknesses in AI reasoning capabilities.
The discussion underscores the need for a shift from mere scaling to more innovative approaches in AI development.
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