Voyager: An Open-Ended Embodied Agent with Large Language Models

Long-term learning and skill library

Foundational work on LLM-based agents and Long-term learning and skill library.

Addresses how language models can extend beyond single-turn generation through Long-term learning and skill library.

The paper operationalizes Long-term learning and skill library through a structured agent mechanism.

The system connects an LLM with state, control, tools, memory, environments, interfaces, or other agents.

Uses task-specific datasets, environments, benchmarks, or interaction traces appropriate to the research question.

Uses the training or inference adaptation documented by the primary source; undisclosed details are not inferred.

Evaluates the proposed mechanism against relevant baselines in the target setting.

Compares against the underlying model, simpler control strategies, or prior agent mechanisms.

Reports evidence for the proposed contribution within its stated benchmark or environment.

Contributes a reusable pattern for building or evaluating LLM-based agents.

Primary source: https://arxiv.org/abs/2305.16291