Lifelong Learning of Large Language Model based Agents: A Roadmap (2501.07278v1)

Abstract: Lifelong learning, also known as continual or incremental learning, is a crucial component for advancing AGI by enabling systems to continuously adapt in dynamic environments. While LLMs have demonstrated impressive capabilities in natural language processing, existing LLM agents are typically designed for static systems and lack the ability to adapt over time in response to new challenges. This survey is the first to systematically summarize the potential techniques for incorporating lifelong learning into LLM-based agents. We categorize the core components of these agents into three modules: the perception module for multimodal input integration, the memory module for storing and retrieving evolving knowledge, and the action module for grounded interactions with the dynamic environment. We highlight how these pillars collectively enable continuous adaptation, mitigate catastrophic forgetting, and improve long-term performance. This survey provides a roadmap for researchers and practitioners working to develop lifelong learning capabilities in LLM agents, offering insights into emerging trends, evaluation metrics, and application scenarios. Relevant literature and resources are available at \href{this url}{https://github.com/qianlima-lab/awesome-lifelong-LLM-agent}.

• The paper introduces a comprehensive roadmap for integrating lifelong learning into LLM-based agents by partitioning the architecture into perception, memory, and action modules.
• The paper details methods for both single-modal and multi-modal perception, along with strategies like prompt compression and experience replay to process diverse data types.
• The paper emphasizes mitigating catastrophic forgetting through regularization and replay-based techniques while utilizing structured memory hierarchies for improved long-term performance.

The paper "Lifelong Learning of LLM \based Agents: A Roadmap" introduces a review of techniques to incorporate lifelong learning into LLM-based agents. It categorizes the agent's core components into perception, memory, and action modules, which facilitate continuous adaptation, mitigation of catastrophic forgetting, and improved long-term performance.

The paper addresses key research questions related to the concepts, architectures, and processes of LLM agents designed for lifelong learning. It investigates how these agents perceive and process data from single and multiple modalities to adapt to new environments and tasks. The paper also explores strategies to mitigate catastrophic forgetting and retain previously learned knowledge, as well as how LLM agents perform various actions in dynamic environments.

The formal definition of lifelong learning for LLM-based agents is provided, modeling the environment as a goal-conditional Partially Observable Markov Decision Process (POMDP) defined by the tuple $$\mathcal{E} = (\mathcal{S}, \mathcal{A}, \mathcal{G}, T, R, \Omega, O, \gamma)$$.

• $\mathcal{S}$ is a set of states.
• $\mathcal{A}$ is a set of actions.
• $\mathcal{G}$ is a set of possible goals.
• $T(s' \mid s,a)$ is the state transition probability function.
• $$R : \mathcal{S} \times \mathcal{A} \times \mathcal{G} \to \mathcal{R}$$ is the goal-conditional reward function.
• $\Omega$ is a set of observations.
• $O(o' \mid s',a)$ is the observation probability function.
• $\gamma \in [0,1)$ is the discount factor.

An LLM-based agent's policy $\pi$ maps observations to actions, where $\pi(o_t) \in \mathcal{A}$ represents the action selected at time step $t$ based on observation $o_t \in \Omega$.

The paper outlines the historical development of lifelong learning in AI systems, from foundational concepts in the 1980s to recent integrations with LLMs and LLM agents. It identifies key stages: establishment of foundational concepts, advancements in deep lifelong learning, integration of lifelong learning into LLMs, and recent developments in lifelong learning for LLM agents.

The overall architecture comprises three key modules: Perception, Memory, and Action. The Perception module continuously gathers information, divided into single-modal (textual) and multi-modal integration. The Memory module stores knowledge, categorized into Working Memory, Episodic Memory, Semantic Memory, and Parametric Memory. The Action module guides interactions, including grounding, retrieval, and reasoning actions.

Single-modal perception primarily involves receiving textual information from web pages and game environments. Methods extract structured text from standardized formats like HTML, or convert screenshots into textual formats. LLM-based agents perceive their surroundings through textual mediums in game environments, recognizing characters, time, location, events, and emotions to perform corresponding actions.

Multi-modal perception involves the agent's ability to integrate information from multiple modalities. Methods are categorized into new knowledge perception and old knowledge perception. New knowledge perception is further divided into Modality-Complete Learning and Modality-Incomplete Learning. Modality-Complete Learning assumes all data has the same modality during training and inference. Modality-Incomplete Learning focuses on how the agent dynamically adapts to learn and infer when encountering incomplete or missing modality information. Old knowledge perception focuses on retaining and perceiving old knowledge as the agent receives multimodal information, using regularization-based and replay-based approaches to mitigate catastrophic forgetting.

Working Memory is primarily the agent's short-term memory, including prompts, workspace memory, and user context. It is discussed from perspectives of prompt compression (soft and hard compression), long context comprehension (context selection and aggregation), role playing (single and multi-agent), self-correction (relying on feedback, assessing confidence, using external tools), and prompt optimization (evolutionary algorithms, Monte Carlo Tree Search).

Episodic memory stores past experiences. It is analyzed from the perspectives of data replay and feature replay (experience replay and generative replay), continual reinforcement learning (experience replay), and self-experience (triplets, databases, documents, conversations).

Semantic memory serves as an external memory mechanism, focusing on continual knowledge graph learning (replay-based, regularization-based, and architecture-based approaches) and continual document learning (document-level and chunk-level updates in Retrieval-Augmented Generation (RAG) applications).

Parametric memory is the knowledge encoded in the LLM's internal parameters. Analysis is done from the perspectives of continual instruction tuning (specific and general capabilities, self-evolution), continual knowledge editing (external memorization, global optimization, local modification), and continual alignment (preference optimization, multi-step alignment).

The action module enables the agent to interact with its environment, make decisions, and execute behaviors. Actions are categorized into grounding actions, retrieval actions, and reasoning actions. Grounding actions involve physically or digitally affecting the environment. Retrieval actions enable the agent to access information from its memory. Reasoning actions involve using working memory, past experiences, and external data for complex tasks.

The paper references multiple works related to multi-modal perception [chen2020soundspaces, zhang2021repetitive, kazakos2019epic], knowledge distillation [gou2021knowledge, gupta2016cross], MoE [masoudnia2014mixture], and more.