# Reproducibility Report: La-MAML: Look-ahead Meta Learning for Continual Learning

arXiv: https://arxiv.org/abs/2102.05824 · HTML: https://mlsanity.com/paper/2102.05824/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference](https://mlsanity.com/paper/1810.11910/) (2018)
- [La-MAML: Look-ahead Meta Learning for Continual Learning](https://mlsanity.com/paper/2007.13904/) (2020)

## Built on by (most cited first, 30 of 42)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Co2L: Contrastive Continual Learning](https://mlsanity.com/paper/2106.14413/) (2021)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [An Empirical Investigation of the Role of Pre-training in Lifelong Learning](https://mlsanity.com/paper/2112.09153/) (2021)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation](https://mlsanity.com/paper/2302.14290/) (2023)
- [Learngene: From Open-World to Your Learning Task](https://mlsanity.com/paper/2106.06788/) (2021)
- [TPO: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees](https://mlsanity.com/paper/2410.12854/) (2024)
- [Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning](https://mlsanity.com/paper/2110.02600/) (2021)
- [Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning](https://mlsanity.com/paper/2308.09544/) (2023)
- [A contrastive rule for meta-learning](https://mlsanity.com/paper/2104.01677/) (2021)
- [SPeCiaL: Self-Supervised Pretraining for Continual Learning](https://mlsanity.com/paper/2106.09065/) (2021)
- [When Meta-Learning Meets Online and Continual Learning: A Survey](https://mlsanity.com/paper/2311.05241/) (2023)
- [Random Representations Outperform Online Continually Learned Representations](https://mlsanity.com/paper/2402.08823/) (2024)
- [HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts](https://mlsanity.com/paper/2305.18421/) (2023)
- [Learning to Continually Learn with the Bayesian Principle](https://mlsanity.com/paper/2405.18758/) (2024)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation](https://mlsanity.com/paper/2206.07260/) (2022)
- [Partial Hypernetworks for Continual Learning](https://mlsanity.com/paper/2306.10724/) (2023)
- [A Layer Selection Approach to Test Time Adaptation](https://mlsanity.com/paper/2404.03784/) (2024)
- [Vision-Language Navigation with Continual Learning](https://mlsanity.com/paper/2409.02561/) (2024)
- [Lookbehind-SAM: k steps back, 1 step forward](https://mlsanity.com/paper/2307.16704/) (2023)
- [DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning](https://mlsanity.com/paper/2305.07892/) (2023)
- [VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference](https://mlsanity.com/paper/2309.08227/) (2023)
- [Dynamic Prototype Rehearsal for Continual ECG Arrhythmia Detection](https://mlsanity.com/paper/2501.07555/) (2025)
- [Integral Continual Learning Along the Tangent Vector Field of Tasks](https://mlsanity.com/paper/2211.13108/) (2022)
- [Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning](https://mlsanity.com/paper/2301.04584/) (2023)
- [KrADagrad: Kronecker Approximation-Domination Gradient Preconditioned Stochastic Optimization](https://mlsanity.com/paper/2305.19416/) (2023)
- [Mitigating Catastrophic Forgetting for Few-Shot Spoken Word Classification Through Meta-Learning](https://mlsanity.com/paper/2305.13080/) (2023)
- [Parabolic Continual Learning](https://mlsanity.com/paper/2503.02117/) (2025)

## Abstract (arXiv)

The Continual Learning (CL) problem involves performing well on a sequence of tasks under limited compute. Current algorithms in the domain are either slow, offline or sensitive to hyper-parameters. La-MAML, an optimization-based meta-learning algorithm claims to be better than other replay-based, prior-based and meta-learning based approaches. According to the MER paper [1], metrics to measure performance in the continual learning arena are Retained Accuracy (RA) and Backward Transfer-Interference (BTI). La-MAML claims to perform better in these values when compared to the SOTA in the domain. This is the main claim of the paper, which we shall be verifying in this report.