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. Large neural networks may learn functions far simpler than their parameter counts suggest, but it is challenging to construct codes that realize this simplicity. Parameter-based methods such as quantization produce code lengths that scale with model size, insensitive to how much information the parameters store. Prequential coding bypasses this issue by compressing the training trajectory, but codes the exact data sequence regardless o
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 52%openvinotoolkit/nncf →
- PossiblePossibly related (embedding) · 50%Tencent/AngelSlim →
- PossiblePossibly related (embedding) · 49%Quantization →
- PossiblePossibly related (embedding) · 47%qualcomm/aimet →
- PossiblePossibly related (embedding) · 46%Dataset of permissively licensed code released →
- LinkedLinked via arxiv author · 85%Shikai Qiu →
“Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data”
- LinkedLinked via arxiv author · 85%Marc Finzi →
“Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data”
- LinkedLinked via arxiv author · 85%Yujia Zheng →
“Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data”