# Entropy-Based Self-Reflective Learning Framework for Language Models

**URL:** <https://discuss.huggingface.co/t/entropy-based-self-reflective-learning-framework-for-language-models/168447>\
**Category:** 🤗Transformers\
**Created:** [September 15, 2025, 3:44pm UTC](https://discuss.huggingface.co/t/entropy-based-self-reflective-learning-framework-for-language-models/168447 "2025-09-15T15:44:13Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![rongxianlingyi](https://avatars.discourse-cdn.com/v4/letter/r/8e8cbc/32.png) [@rongxianlingyi](https://discuss.huggingface.co/u/rongxianlingyi)\
**Post date:** [September 15, 2025, 3:44pm UTC](https://discuss.huggingface.co/t/entropy-based-self-reflective-learning-framework-for-language-models/168447/1 "2025-09-15T15:44:13Z")

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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

🧠 We call it: **Entropy-Driven Self-Reflective Optimization**  
⏳ 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.