Data Attribution in Adaptive Learning
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913008084582400 |
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| author | Rege, Amit Kiran |
| author_facet | Rege, Amit Kiran |
| contents | Machine learning models increasingly generate their own training data -- online bandits, reinforcement learning, and post-training pipelines for language models are leading examples. In these adaptive settings, a single training observation both updates the learner and shifts the distribution of future data the learner will collect. Standard attribution methods, designed for static datasets, ignore this feedback. We formalize occurrence-level attribution for finite-horizon adaptive learning via a conditional interventional target, prove that replay-side information cannot recover it in general, and identify a structural class in which the target is identified from logged data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04892 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data Attribution in Adaptive Learning Rege, Amit Kiran Machine Learning Machine learning models increasingly generate their own training data -- online bandits, reinforcement learning, and post-training pipelines for language models are leading examples. In these adaptive settings, a single training observation both updates the learner and shifts the distribution of future data the learner will collect. Standard attribution methods, designed for static datasets, ignore this feedback. We formalize occurrence-level attribution for finite-horizon adaptive learning via a conditional interventional target, prove that replay-side information cannot recover it in general, and identify a structural class in which the target is identified from logged data. |
| title | Data Attribution in Adaptive Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.04892 |