CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization (2310.10134v1)

Abstract: Language agents have shown some ability to interact with an external environment, e.g., a virtual world such as ScienceWorld, to perform complex tasks, e.g., growing a plant, without the startup costs of reinforcement learning. However, despite their zero-shot capabilities, these agents to date do not continually improve over time beyond performance refinement on a specific task. Here we present CLIN, the first language-based agent to achieve this, so that it continually improves over multiple trials, including when both the environment and task are varied, and without requiring parameter updates. Our approach is to use a persistent, dynamic, textual memory centered on causal abstractions (rather than general "helpful hints") that is regularly updated after each trial so that the agent gradually learns useful knowledge for new trials. In the ScienceWorld benchmark, CLIN is able to continually improve on repeated trials on the same task and environment, outperforming state-of-the-art reflective language agents like Reflexion by 23 absolute points. CLIN can also transfer its learning to new environments (or new tasks), improving its zero-shot performance by 4 points (13 for new tasks) and can further improve performance there through continual memory updates, enhancing performance by an additional 17 points (7 for new tasks). This suggests a new architecture for agents built on frozen models that can still continually and rapidly improve over time.

• The paper presents a novel causal memory framework enabling CLIN to learn task-specific actions from dynamic textual feedback.
• CLIN outperforms reflective agents by 23 percentage points on ScienceWorld and shows up to a 13-point improvement on new task trials.
• CLIN employs a nonparametric architecture that quickly adapts without parameter updates, advancing lifelong learning in AI systems.

Overview of CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization

The paper introduces CLIN, a continually learning language agent designed to enhance its performance over time through successive trials, adapting not only to changes in its environment but also to new tasks. Unlike traditional reinforcement learning approaches that rely on extensive training and model fine-tuning, CLIN leverages goal-driven interactions within a virtual environment such as ScienceWorld, a platform designed for performing complex science-based tasks, to improve without requiring parameter updates.

Key Contributions

1. Causal Memory Framework: CLIN employs a dynamic textual memory system centered on causal abstractions rather than general insights. This memory evolves continually with each trial. The use of causal abstractions allows the agent to learn actions that contribute significantly to specific state transitions and goals.
2. Contemporary Benchmarking: Evaluated against the ScienceWorld benchmark, CLIN demonstrated significant improvements in performance, exceeding leading reflective language agents like Reflexion by 23 percentage points. It not only shows enhancements on repeated attempts of the same task but also exhibits improved performance when tested on new tasks and in new environments, achieving a 4-point improvement for new environments and a 13-point improvement for new task trials.
3. Nonparametric Learning Architecture: CLIN is posited as a novel architecture for frozen models. It achieves rapid adaptation and generalization without necessitating parameter modification, addressing a critical need for efficiency in the deployment of capable AI agents.

Methodological Insights

The architecture of CLIN involves three core modules: a memory system, a controller to determine the next goal, and an executor for action generation. Learning is facilitated by a fourth component – the memory generator, which updates memory content based on experiments and interactions. CLIN's ability to reflect on interactions to refine its causal understanding allows it to bypass the steep sample efficiency requirements characterizing reinforcement learning.

Memory Generation and Generalization:

• The system uses a LLM to continuously synthesize and update actionable causal abstractions based on trial feedback and past experiences.
• For adaptation in new environments, CLIN formulates a 'meta-memory' that summarizes pivotal memories from past trials, allowing it to abstract general knowledge applicable across different scenarios.

Impact on Future AI Development

The evidence presented indicates that dynamically evolving memory can significantly enhance performance without relying on parameter tuning. This aligns with key challenges in AI, such as developing agents capable of lifelong learning and effective knowledge transfer without exhaustive retraining. The specific focus on causal relationships and dynamic memory presents a promising avenue for pushing the boundaries of AI capabilities, moving towards more intelligent, adaptable, and efficient AI systems.

While the results are promising, they also point toward the need for improved mechanisms in memory retrieval and handling uncertainty in variable environments. For broader applicability, further refinement in integrating insights from diverse task interactions could enable CLIN to tackle increasingly complex multi-step tasks across domains.

In conclusion, CLIN's approach represents a significant stride towards achieving robust task adaptation and generalization in AI agents, setting the stage for further innovations in the domain of intelligent, learning-capable AI systems.