Small-to-Large Generalization: Training Data Influences Models Consistently Across Scale

Alaa Khaddaj, Logan Engstrom, A. Ma̧dry
2/12/2026

No confident code implementation found for this paper. 1 possible match can be shown below.

Semantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

KeypointDETR: An End-to-End 3D Keypoint Detector

99% match1 repo282026

Generating Handwritten Mathematical Expressions From Symbol Graphs: An End-to-End Pipeline

98% match2026

Think Then React: Towards Unconstrained Action-to-Reaction Motion Generation

98% match1 repo222026

AutoCATE: End-to-End, Automated Treatment Effect Estimation

97% match2026

Comparing Few to Rank Many: Active Human Preference Learning Using Randomized Frank-Wolfe Method

97% match1 repo2026

MPOD123: One Image to 3D Content Generation Using Mask-Enhanced Progressive Outline-to-Detail Optimization

97% match2026

Cite this paper

@article{khaddaj2026smalltolarge,
  title  = {Small-to-Large Generalization: Training Data Influences Models Consistently Across Scale},
  author = {Alaa Khaddaj and Logan Engstrom and A. Ma̧dry},
  year   = {2026},
  url    = {https://api.semanticscholar.org/CorpusID:3a432a2dd9a99832662aa9aba43428cea36206dc},
  journal = {ICLR 2025 2025}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/3a432a2dd9a99832662aa9aba43428cea36206dc.svg)](https://codeofpaper.com/paper/3a432a2dd9a99832662aa9aba43428cea36206dc)

Discussion