mlsanityData to 7 Oct 2026

Datasets for Studying Generalization from Easy to Hard Examples

arXiv 2108.06011 ↗First posted 13 Aug 2021cs.LG cs.AIcited by 12

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Abstract (arXiv)

We describe new datasets for studying generalization from easy to hard examples.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 0 of its 2 references that are in the map. First posted on arXiv 13 Aug 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 2; 2024: 4; 2025: 1; 2026: 2.

12 papers.

  1. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
  2. 2025Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approachcited by 372
  3. 2022End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinkingcited by 33
  4. 2022Path Independent Equilibrium Models Can Better Exploit Test-Time Computationcited by 33
  5. 2023A Configurable Library for Generating and Manipulating Maze Datasetscited by 31
  6. 2024Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalizationcited by 24
  7. 2024Planning in a recurrent neural network that plays Sokobancited by 17
  8. 2024Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraintscited by 8
  9. 2024On Logical Extrapolation for Mazes with Recurrent and Implicit Networkscited by 7
  10. 2022FLOWGEN: Fast and slow graph generationcited by 2
  11. 2026LA-Sign: Looped Transformers with Geometry-aware Alignment for Skeleton-based Sign Language Recognitioncited by 0
  12. 2026AdaStop: Cost-Aware Early Stopping for DNN Test Selectioncited by 0