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[2602.19733] Understanding the Curse of Unrolling
Summary
The paper explores the 'curse of unrolling' in machine learning, detailing how algorithm unrolling can diverge from true Jacobians and proposing solutions to mitigate this issue.
Why It Matters
Understanding the curse of unrolling is crucial for improving the accuracy of machine learning models, particularly in hyperparameter optimization and meta-learning. The findings can lead to more efficient algorithms and better resource management in computational tasks.
Key Takeaways
- Algorithm unrolling can lead to divergence from true Jacobians.
- The curse of unrolling can be mitigated by truncating early iterations.
- Warm-starting in bilevel optimization provides a practical solution.
- The paper includes theoretical analysis supported by numerical experiments.
- Understanding these concepts can enhance model performance and efficiency.
Computer Science > Machine Learning arXiv:2602.19733 (cs) [Submitted on 23 Feb 2026] Title:Understanding the Curse of Unrolling Authors:Sheheryar Mehmood, Florian Knoll, Peter Ochs View a PDF of the paper titled Understanding the Curse of Unrolling, by Sheheryar Mehmood and 2 other authors View PDF HTML (experimental) Abstract:Algorithm unrolling is ubiquitous in machine learning, particularly in hyperparameter optimization and meta-learning, where Jacobians of solution mappings are computed by differentiating through iterative algorithms. Although unrolling is known to yield asymptotically correct Jacobians under suitable conditions, recent work has shown that the derivative iterates may initially diverge from the true Jacobian, a phenomenon known as the curse of unrolling. In this work, we provide a non-asymptotic analysis that explains the origin of this behavior and identifies the algorithmic factors that govern it. We show that truncating early iterations of the derivative computation mitigates the curse while simultaneously reducing memory requirements. Finally, we demonstrate that warm-starting in bilevel optimization naturally induces an implicit form of truncation, providing a practical remedy. Our theoretical findings are supported by numerical experiments on representative examples. Subjects: Machine Learning (cs.LG); Optimization and Control (math.OC) Cite as: arXiv:2602.19733 [cs.LG] (or arXiv:2602.19733v1 [cs.LG] for this version) https://doi.org/10.48550...
