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 oncausal 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.