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.
Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data
Compression is fundamental to intelligence. A model that can represent its training data as a short code has discovered regularities that enable generalization.
Related stories
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.
arXiv Machine Learning
Sep 21
On the Limits of Maximal Coding Rate Reduction for Out-of-Distribution Generalisation
The paper investigates the limits of the maximal coding rate reduction (MCR²) framework for out‑of‑distribution (OOD) generalisation. It shows that MCR² can lead to complete prediction failure under distribution shift, even when a perfectly stable feature is available, and that adding invariance principles from IRM or REx does not resolve this issue. The authors conclude that additional assumptions or learning principles are needed to guarantee stable OOD predictions with MCR².
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.
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.
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.
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.
arXiv AI
Aug 5
On the Limits of Layer Pruning for Generative Reasoning in Large Language Models
arXiv:2602. 01997v3 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classification benchmarks, often with little or no finetuning.
arXiv AI
6d ago
Teacher-Anchored Selection of Post-Training Quantized Models under Domain Shift
The paper investigates how to choose the best quantized model from a family of compressed versions when target labels are scarce or unavailable. It finds that a simple rule based on minimum teacher distortion consistently selects the same eight‑bit, per‑channel, unclipped configuration, though this does not minimize empirical target cross‑entropy. The study also shows that confidence‑based estimators perform poorly in overconfident regimes, while output‑distribution estimators can outperform the teacher in some architectures, and that combining distortion with a supervised term can improve selection. Across 134 candidate families, teacher‑anchored selection reduces mean regret with very few labels, though the benefit diminishes after about 25 labels.
arXiv AI
Sep 24
Uncheatable Eval: Dynamic Compression-Based Evaluation of Language Models
arXiv:2609.27510v1 Announce Type: cross Abstract: Modern large language models are pretrained on massive datasets, making it difficult to prevent benchmark data from entering their training sets and...
arXiv Machine Learning
Jul 21
EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database
arXiv:2607. 18187v1 Announce Type: cross Abstract: Large-scale scientific simulations generate volumetric data at rates that far outpace advances in storage and network bandwidth, making effective lossy compression increasingly critical.
Hugging Face Trending Papers
Jul 28
Bridging Compute- and Data-Optimal Pretraining
Classical compute-optimal scaling laws assume an unbounded supply of fresh pretraining data, yet pretraining is increasingly entering a regime in which compute grows faster than the availability of high-quality data. We propose Compute-Data (CD) scaling laws, a unified framework that bridges compute-optimal scaling, where data scales freely with compute, and data-optimal scaling, where the corpus is fixed while compute can grow without bound.