Researchers Exploring Structured Wrongness and Blind Reconstruction
The post discusses a research paper on
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Why it matters
This research explores novel approaches to language model training and reasoning that could lead to more efficient and powerful AI systems.
Key Points
- 1Exploring training a model to produce structured, coherent
- 2 instead of random garbage
- 3Training a second model to reconstruct the original intent from the anti-correlated output
- 4Hypothesis that the
- 5 of the problem may be preserved through negation, enabling efficient reasoning
- 6Potential to bypass the bottleneck of language model tokenization and reasoning in semantic space
Details
The post discusses a research paper that explores the idea of training a language model (Model A) to produce maximally anti-correlated output - structured wrongness that violates every assumption and design decision, but in a coherent way. The goal is for this model to encode the original intent in a transformed representation. A second model (Model B) would then be trained solely on the output of Model A, without any knowledge of the original prompts, to try and reconstruct the original intent. The key hypothesis is that the
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