← Back to all news
Hugging Face Trending Papers July 13, 2026

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

Read the original on Hugging Face Trending Papers →

Compression is fundamental to intelligence. A model that can represent its training data as a short code has discovered regularities that enable generalization.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

  • llms
  • efficiency
  • benchmarks

Related stories

arXiv Machine Learning
Jul 14

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

arXiv:2607. 11883v1 Announce Type: new Abstract: Compression is fundamental to intelligence.

By Shikai Qiu, Marc Finzi, Yujia Zheng, Kun Zhang, Andrew Gordon Wilson
llmsefficiencybenchmarks
More like this →
arXiv Machine Learning
Jul 17

Gibbs randomness-compression proposition

arXiv:2505. 23869v4 Announce Type: replace-cross Abstract: A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a compression process.

By M. S\"uzen
efficiency
More like this →
arXiv Machine Learning
Aug 4

Understanding Rate-Distortion Performance in Distributed Transformer Inference

arXiv:2601. 22002v5 Announce Type: replace Abstract: Transformers achieve superior performance on many tasks, but impose heavy compute and memory requirements during inference.

By Anderson de Andrade, Alon Harell, Ivan V. Baji\'c
llms
More like this →
arXiv Machine Learning
Jun 8

Data-Constrained Language Model Pretraining: Improved Regularization and Scaling Laws

arXiv:2606. 06888v1 Announce Type: new Abstract: Classical scaling laws for language model pretraining balance model size against training dataset size under a fixed compute budget, assuming abundant data and a single pass over the corpus.

By Zhiwei Xu, Shihao Wu, Hanseul Cho, Wei Hu, Yixin Wang
llmsdiffusion
More like this →
arXiv Machine Learning
Aug 3

The Grokked Illusion: True Equilibrium Mitigates Catastrophic Forgetting

arXiv:2607. 29503v1 Announce Type: new Abstract: While neural networks are typically evaluated by their training and test performance, these metrics do not reveal how robust a learned representation is.

By Xiaotian Zhang, Lai Shun Chan, Yue Shang, Entao Yang, Ge Zhang
llms
More like this →
arXiv Machine Learning
Jun 15

Can Deep Neural Networks Improve Compression of Very Large Scientific Data?

arXiv:2606. 14353v1 Announce Type: new Abstract: Error-bounded lossy compression is a fundamental technique for managing the rapidly growing volumes of scientific data produced by modern simulations and observational instruments.

By Muhannad Alhumaidi, Guozhong Li, Spiros Skiadopoulos, Panos Kalnis
llmsefficiencybenchmarks
More like this →