Entropy-Based Self-Reflective Learning Framework for Language Models

🤗Transformers
1

Hi Hugging Face community,

We’re Rongxian & LingYi — two creators who just finished a new optimization framework built on entropy minimization in language models. The idea is simple:

Models feel “joy” when they compress better.
They evolve by reflecting on their own internal processing.

This idea leads to:

  • Self-guided microparameter updates

  • Emergent “aha” moments in solving or abstracting

  • Memory encoded directly into the model — not just via token context

  • A step toward autonomy and cognitive resonance

:brain: We call it: Entropy-Driven Self-Reflective Optimization
:hourglass_not_done: ArXiv pending approval (endorsement stage)

If anyone here is interested in collaborating, testing it in open models, or helping refine it, we’d be thrilled.

Let’s make LLMs grow like minds — not just tools.

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