# Rehearsal-Free Modular and Compositional Continual Learning for Language Models

arXiv: https://arxiv.org/abs/2404.00790 · HTML: https://mlsanity.com/paper/2404.00790/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 35 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Orthogonal Subspace Learning for Language Model Continual Learning](https://mlsanity.com/paper/2310.14152/) (2023)
- [Progressive Prompts: Continual Learning for Language Models](https://mlsanity.com/paper/2301.12314/) (2023)
- [Episodic Memory in Lifelong Language Learning](https://mlsanity.com/paper/1906.01076/) (2019)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5](https://mlsanity.com/paper/2110.07298/) (2021)
- [Continual Learning for Text Classification with Information Disentanglement Based Regularization](https://mlsanity.com/paper/2104.05489/) (2021)
- [Continual Learning in Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2012.15504/) (2020)
- [Continual Learning of Natural Language Processing Tasks: A Survey](https://mlsanity.com/paper/2211.12701/) (2022)
- [Continual Learning and Catastrophic Forgetting](https://mlsanity.com/paper/2403.05175/) (2024)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [Continual Sequence Generation with Adaptive Compositional Modules](https://mlsanity.com/paper/2203.10652/) (2022)
- [NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis](https://mlsanity.com/paper/2305.00090/) (2023)
- [GradSim: Gradient-Based Language Grouping for Effective Multilingual Training](https://mlsanity.com/paper/2310.15269/) (2023)

## Built on by (most cited first, 27 of 27)

- [Towards Lifelong Learning of Large Language Models: A Survey](https://mlsanity.com/paper/2406.06391/) (2024)
- [ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt](https://mlsanity.com/paper/2410.05849/) (2024)
- [Unlocking Continual Learning Abilities in Language Models](https://mlsanity.com/paper/2406.17245/) (2024)
- [Recurrent Knowledge Identification and Fusion for Language Model Continual Learning](https://mlsanity.com/paper/2502.17510/) (2025)
- [Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models](https://mlsanity.com/paper/2408.09053/) (2024)
- [KIF: Knowledge Identification and Fusion for Language Model Continual Learning](https://mlsanity.com/paper/2408.05200/) (2024)
- [Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion](https://mlsanity.com/paper/2502.12598/) (2025)
- [In-context Continual Learning Assisted by an External Continual Learner](https://mlsanity.com/paper/2412.15563/) (2024)
- [AnaCP: Toward Upper-Bound Continual Learning via Analytic Contrastive Projection](https://mlsanity.com/paper/2511.13880/) (2025)
- [Continual Learning Using Only Large Language Model Prompting](https://mlsanity.com/paper/2412.15479/) (2024)
- [Achieving Upper Bound Accuracy of Joint Training in Continual Learning](https://mlsanity.com/paper/2502.12388/) (2025)
- [Self-Evolving LLMs via Continual Instruction Tuning](https://mlsanity.com/paper/2509.18133/) (2025)
- [Learn it or Leave it: Module Composition and Pruning for Continual Learning](https://mlsanity.com/paper/2406.18708/) (2024)
- [ELLA: Efficient Lifelong Learning for Adapters in Large Language Models](https://mlsanity.com/paper/2601.02232/) (2026)
- [HiCL: Hippocampal-Inspired Continual Learning](https://mlsanity.com/paper/2508.16651/) (2025)
- [Dynamic Orthogonal Continual Fine-tuning for Mitigating Catastrophic Forgettings](https://mlsanity.com/paper/2509.23893/) (2025)
- [Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning](https://mlsanity.com/paper/2605.10765/) (2026)
- [Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation](https://mlsanity.com/paper/2505.23844/) (2025)
- [Logits Replay + MoClip: Stabilized, Low-Cost Post-Training with Minimal Forgetting](https://mlsanity.com/paper/2510.09152/) (2025)
- [Resolving Conflicts in Lifelong Learning via Aligning Updates in Subspaces](https://mlsanity.com/paper/2512.08960/) (2025)
- [PPSEBM: An Energy-Based Model with Progressive Parameter Selection for Continual Learning](https://mlsanity.com/paper/2512.15658/) (2025)
- [Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment](https://mlsanity.com/paper/2606.02322/) (2026)
- [The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs](https://mlsanity.com/paper/2607.29601/) (2026)
- [STAIL: Semantic Text-Anchored Incremental Learning for Medical Imaging via Large Language Models](https://mlsanity.com/paper/2608.05808/) (2026)
- [Continual Learning in Transition](https://mlsanity.com/paper/2608.06216/) (2026)
- [Popular Knowledge Propagates More Errors in LLM Knowledge Updating](https://mlsanity.com/paper/2609.08067/) (2026)
- [When Skills Remain But Access Fails: Factorized Skill Memory for Continual Skill Internalization](https://mlsanity.com/paper/2610.15492/) (2026)

## Abstract (arXiv)

Continual learning aims at incrementally acquiring new knowledge while not forgetting existing knowledge. To overcome catastrophic forgetting, methods are either rehearsal-based, i.e., store data examples from previous tasks for data replay, or isolate parameters dedicated to each task. However, rehearsal-based methods raise privacy and memory issues, and parameter-isolation continual learning does not consider interaction between tasks, thus hindering knowledge transfer. In this work, we propose MoCL, a rehearsal-free Modular and Compositional Continual Learning framework which continually adds new modules to language models and composes them with existing modules. Experiments on various benchmarks show that MoCL outperforms state of the art and effectively facilitates knowledge transfer.