arXiv:2602.15829v2 Announce Type: replace Abstract: The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that post-training merely surfaces this knowledge. The SAH, however, lacks a precise definition, which has led to (i) different and seemingly orthogonal arguments supporting it, and (ii) important critiques to it. We propose a new metric called task complexity: the length of the shortest program that achieves a target performance on a task. In this framework, the SAH simply claims that pre-trained models drastica
The increasing sophistication of large language models and widespread debate on their true intelligence and learning mechanisms necessitates more rigorous theoretical frameworks.
A clearer understanding of how LLMs acquire knowledge and the role of post-training can significantly influence future AI research, development, and application strategies, particularly concerning AI safety and capabilities.
This research introduces 'task complexity' as a metric, providing a more precise and testable definition for the superficial alignment hypothesis, moving the discussion from qualitative arguments to a quantifiable framework.
- · AI researchers and theoreticians
- · Developers focused on model explainability
- · AI safety institutions
- · Unstructured AI philosophical debates
- · Companies relying on opaque LLM capabilities without understanding core mechanis
The adoption of 'task complexity' could standardize evaluations of LLM learning and knowledge acquisition.
Improved theoretical understanding may lead to more efficient and targeted training methodologies for complex AI tasks.
More predictable and robust AI systems, potentially accelerating commercial deployment with greater trustworthiness.
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Read at arXiv cs.LG