Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data — mentioned by pavelai

Fri, 17 Jul 2026 13:01:39 GMT

"We introduce requential coding, where a teacher model selects training samples drawn from the student's own distribution" Due to new learning technique the model has achieved better generalization skill without overfitting and memorization. This become possible because of new learning method which made the student model to generate samples for itself. It led to intensive reuse of existing neurons and allowed to encode information in a more dense way While researchers are calling it a compression, I think it's a retopologization, and Microsoft had tried to do something similar in the past…

Paper and discussion history. A mention is not an endorsement.

Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data · HackerPapers discussions

Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data — mentioned by pavelai