# nabla τ: Gradient-based and Task-Agnostic machine Unlearning

arXiv: https://arxiv.org/abs/2403.14339 · HTML: https://mlsanity.com/paper/2403.14339/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Backdoor Learning: A Survey](https://mlsanity.com/paper/2007.08745/) (2020)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [Unrolling SGD: Understanding Factors Influencing Machine Unlearning](https://mlsanity.com/paper/2109.13398/) (2021)
- [Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher](https://mlsanity.com/paper/2205.08096/) (2022)
- [Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening](https://mlsanity.com/paper/2308.07707/) (2023)
- [Machine Unlearning for Random Forests](https://mlsanity.com/paper/2009.05567/) (2020)
- [Machine Unlearning: Linear Filtration for Logit-based Classifiers](https://mlsanity.com/paper/2002.02730/) (2020)

## Built on by (most cited first, 11 of 11)

- [Multi-Objective Large Language Model Unlearning](https://mlsanity.com/paper/2412.20412/) (2024)
- [Unlearning Personal Data from a Single Image](https://mlsanity.com/paper/2407.12069/) (2024)
- [DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs](https://mlsanity.com/paper/2504.13774/) (2025)
- [Quantum-Inspired Audio Unlearning: Towards Privacy-Preserving Voice Biometrics](https://mlsanity.com/paper/2507.22208/) (2025)
- [Superior resilience to poisoning and amenability to unlearning in quantum machine learning](https://mlsanity.com/paper/2508.02422/) (2025)
- [Conformal Unlearning: A New Paradigm for Unlearning in Conformal Predictors](https://mlsanity.com/paper/2508.03245/) (2025)
- [Grokked Models are Better Unlearners](https://mlsanity.com/paper/2512.03437/) (2025)
- [Distribution-Guided and Constrained Quantum Machine Unlearning](https://mlsanity.com/paper/2601.04413/) (2026)
- [Structural Disentanglement in Bilinear MLPs via Architectural Inductive Bias](https://mlsanity.com/paper/2602.05635/) (2026)
- [Graph Propagated Projection Unlearning: A Unified Framework for Vision and Audio Discriminative Models](https://mlsanity.com/paper/2604.13127/) (2026)
- [LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats](https://mlsanity.com/paper/2607.16227/) (2026)

## Abstract (arXiv)

Machine Unlearning, the process of selectively eliminating the influence of certain data examples used during a model's training, has gained significant attention as a means for practitioners to comply with recent data protection regulations. However, existing unlearning methods face critical drawbacks, including their prohibitively high cost, often associated with a large number of hyperparameters, and the limitation of forgetting only relatively small data portions. This often makes retraining the model from scratch a quicker and more effective solution. In this study, we introduce Gradient-based and Task-Agnostic machine Unlearning (nabla τ), an optimization framework designed to remove the influence of a subset of training data efficiently. It applies adaptive gradient ascent to the data to be forgotten while using standard gradient descent for the remaining data. nabla τ offers multiple benefits over existing approaches. It enables the unlearning of large sections of the training dataset (up to 30%). It is versatile, supporting various unlearning tasks (such as subset forgetting or class removal) and applicable across different domains (images, text, etc.). Importantly, nabla τ requires no hyperparameter adjustments, making it a more appealing option than retraining the model from scratch. We evaluate our framework's effectiveness using a set of well-established Membership Inference Attack metrics, demonstrating up to 10% enhancements in performance compared to state-of-the-art methods without compromising the original model's accuracy.