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| license: cc0-1.0
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| tags:
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| - research-notes
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| - data-efficient-learning
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| ---
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| # Research notes: Data Efficient Learning
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| ## Overview
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| This repository contains a working research note about **Data Efficient Learning**. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models.
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| ## Acknowledgments
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| This repo borrows ideas freely from public reference implementations; credit to their authors.
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| ## Open questions
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| - which baselines are actually comparable under matched capacity?
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| - what is the minimum data exposure for a meaningful signal?
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| - which confounders would invalidate a naive comparison?
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| ## Scope and limitations
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| Exploratory by design: no benchmark claims, no completed ablations, no released code, no trained checkpoint. The references are a starting point for verification, not evidence that the study has run.
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| ## Contact
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| Questions or corrections: open a discussion in this repository.
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| ## How to read this repository
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| Read `paper_notes.md` first. Anything labeled as a plan or hypothesis is not a result. Future results should ship with dataset versions, commands, seeds, hardware and raw logs.
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| ## What is covered
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| - research question scope and likely confounders
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| - one proposed comparison against matched baselines
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| - evaluation context such as task-appropriate public benchmarks named in the main note
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| - reproducibility checks and open questions
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| ## Files
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| | File | Purpose |
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| |---|---|
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| | `paper_notes.md` | primary artifact |
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| | `README.md` | this documentation |
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| | `config.json` | architecture configuration |
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| | `training_args.json` | default experiment settings |
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| | `model.safetensors` | initialization checkpoint |
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| | `NOTES.md` | informal to-do notes |
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| ## License
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| Distributed under the **cc0-1.0** license. External datasets keep their own terms — check them separately.
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