[Paper Review] An Introduction to Lifelong Supervised Learning

Shagun Sodhani, Mojtaba Faramarzi+5 more|arXiv (Cornell University)|Jul 10, 2022

Higher Education Learning Practices4 citations

TL;DR

This paper introduces lifelong supervised learning as a framework for enabling AI systems to continuously learn from sequential tasks while mitigating catastrophic forgetting. It proposes modular architectures, adapters, and integration with external knowledge bases to support incremental knowledge acquisition, selective forgetting, and dynamic adaptation—key for real-world deployment of scalable, lifelong AI systems.

ABSTRACT

This primer is an attempt to provide a detailed summary of the different facets of lifelong learning. We start with Chapter 2 which provides a high-level overview of lifelong learning systems. In this chapter, we discuss prominent scenarios in lifelong learning (Section 2.4), provide 8 Introduction a high-level organization of different lifelong learning approaches (Section 2.5), enumerate the desiderata for an ideal lifelong learning system (Section 2.6), discuss how lifelong learning is related to other learning paradigms (Section 2.7), describe common metrics used to evaluate lifelong learning systems (Section 2.8). This chapter is more useful for readers who are new to lifelong learning and want to get introduced to the field without focusing on specific approaches or benchmarks. The remaining chapters focus on specific aspects (either learning algorithms or benchmarks) and are more useful for readers who are looking for specific approaches or benchmarks. Chapter 3 focuses on regularization-based approaches that do not assume access to any data from previous tasks. Chapter 4 discusses memory-based approaches that typically use a replay buffer or an episodic memory to save subset of data across different tasks. Chapter 5 focuses on different architecture families (and their instantiations) that have been proposed for training lifelong learning systems. Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.

Motivation & Objective

• Address the challenge of continual learning in real-world AI systems where data and tasks evolve over time.
• Overcome catastrophic forgetting in machine learning models during sequential task learning.
• Enable efficient, incremental knowledge updates without retraining from scratch.
• Facilitate integration of external knowledge sources to handle dynamically changing information.
• Develop modular, extensible architectures (e.g., adapters) that support backward and forward knowledge transfer.

Proposed method

• Propose a lifelong learning framework that supports forward and backward knowledge transfer through modular parameter updates.
• Utilize adapter modules—small, trainable components inserted into pre-trained transformer models—to enable efficient fine-tuning with minimal parameter updates.
• Enable selective knowledge retention and forgetting by allowing only adapter modules to be updated during new task learning.
• Integrate memory-augmented neural networks (e.g., Neural Turing Machines, Differentiable Neural Computers) for dynamic access to external knowledge stores.
• Design curriculum-based learning pipelines where systems learn to compose skills (e.g., querying knowledge bases, aggregating results) across tasks.
• Combine modular parameterization with external knowledge bases to decouple static knowledge from dynamic, evolving information.

Experimental results

Research questions

• RQ1 How can machine learning models learn sequentially from new tasks without forgetting previously acquired knowledge?
• RQ2 What architectural designs enable efficient backward knowledge transfer while preserving performance on prior tasks?
• RQ3 How can lifelong learning systems effectively query and integrate external, dynamic knowledge sources in real time?
• RQ4 To what extent can adapter-based fine-tuning maintain performance across tasks while minimizing parameter updates?
• RQ5 What role do skill composition and curriculum learning play in enabling generalization and scalability in lifelong learning?

Key findings

• Adapter-based fine-tuning achieves strong performance on downstream tasks while keeping the original pre-trained model weights frozen, enabling compact and extensible lifelong learning systems.
• Modular architectures with adapters support incremental learning and backward transfer by allowing selective updates to specific knowledge modules.
• Integration with external knowledge bases enables systems to answer time-sensitive queries (e.g., current weather) that static models cannot handle.
• Memory-augmented networks like the Differentiable Neural Computer support dynamic access to external information, enhancing adaptability in real-world settings.
• Curriculum-based training enables systems to learn complex, multi-step reasoning tasks by composing previously acquired skills.
• The combination of modular parameterization and external knowledge access provides a scalable inductive bias for lifelong learning in dynamic environments.