Dataset Distillation Fails Without Hard Labels
Soft labels mask poor dataset quality in distillation methods, making random subsets nearly as effective as curated ones.
Source: arxiv/cs.LG · Priyam Dey, Aditya Sahdev, Sunny Bhati, Konda Reddy Mopuri, R. Venkatesh Babu · open original ↗
Soft labels during training hide dataset quality differences, making dataset distillation methods perform no better than random sampling.
- — State-of-the-art distillation methods match random baselines when soft labels are used for downstream training.
- — Hard-label evaluation reveals distillation quality; soft labels obscure performance gaps between curated and random data.
- — In soft-label regimes, subset quality has negligible effect on final model performance regardless of size.
- — Only RDED reliably outperforms random baselines on ImageNet-1K under hard-label conditions.
- — CAD-Prune metric identifies samples of optimal difficulty for a given compute budget.
- — CA2D method combines compute-aware pruning to outperform existing distillation approaches on large-scale benchmarks.
- — Coreset literature findings do not transfer to soft-label settings used in modern distillation research.
Astrobobo tool mapping
- Knowledge Capture Document the label regime (hard/soft/mixed) used in your distillation experiments. Flag any results that rely solely on soft-label evaluation as requiring hard-label validation before deployment.
- Focus Brief Create a one-page checklist: (1) Is distillation evaluated under hard labels? (2) Does subset quality improve hard-label accuracy? (3) What is the compute budget? (4) Does sample difficulty align with budget? Use this before adopting a new distillation method.
- Reading Queue Queue papers on coreset selection and compute-aware pruning to understand why difficulty-based sample selection outperforms generic distillation. Prioritize works comparing hard and soft label regimes.
Frequently asked
- When soft labels from a teacher model are used during training, the knowledge distillation loss regularizes the model and reduces its sensitivity to which samples are selected. This masks poor sample quality. Hard-label evaluation exposes this: distilled subsets often perform no better than random subsets under hard labels, revealing that the method did not actually select better data.
cite ▸
APA
Priyam Dey, Aditya Sahdev, Sunny Bhati, Konda Reddy Mopuri, R. Venkatesh Babu. (2026, April 22). Dataset Distillation Fails Without Hard Labels. Astrobobo Content Engine (rewrite of arxiv/cs.LG). https://astrobobo-content-engine.vercel.app/article/dataset-distillation-fails-without-hard-labels-8a1eb0
MLA
Priyam Dey, Aditya Sahdev, Sunny Bhati, Konda Reddy Mopuri, R. Venkatesh Babu. "Dataset Distillation Fails Without Hard Labels." Astrobobo Content Engine, 22 Apr 2026, https://astrobobo-content-engine.vercel.app/article/dataset-distillation-fails-without-hard-labels-8a1eb0. Based on "arxiv/cs.LG", https://arxiv.org/abs/2604.18811.
BibTeX
@misc{astrobobo_dataset-distillation-fails-without-hard-labels-8a1eb0_2026,
author = {Priyam Dey, Aditya Sahdev, Sunny Bhati, Konda Reddy Mopuri, R. Venkatesh Babu},
title = {Dataset Distillation Fails Without Hard Labels},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/dataset-distillation-fails-without-hard-labels-8a1eb0},
note = {Astrobobo rewrite of arxiv/cs.LG, https://arxiv.org/abs/2604.18811},
}