Coherence optimization is a theoretical framework unifying various language model self-improvement methods, demonstrating they find a maximally compressible and predictable context-to-behavior mapping. It's proven equivalent to description-length regularization, offering an optimal approach for semi-supervised learning.
Coherence optimization is a new theory explaining how AI language models can improve their accuracy without needing human feedback. It shows that various self-improvement methods are essentially finding the simplest and most predictable ways for the model to respond to different inputs. This approach is proven to be ideal for learning with limited data.
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