Voyager: An Open-Ended Embodied Agent with Large Language Models — interactive visual explainer | Rudrite Research

Researchers at arXiv 2023 introduced Voyager, an open-ended embodied agent that uses GPT-4 to propose tasks, write executable code against a game API, verify it, and archive verified programs into a growing skill library. The skills persist, compound, and transfer to unseen worlds, demonstrating a novel approach to lifelong learning in AI.

Voyager: An Open-Ended Embodied Agent with Large Language Models An agent that writes its own tools: GPT-4 proposes tasks, writes executable code against the game API, verifies it works, and archives every verified program into an ever-growing skill library — skills that persist, compound, and transfer to unseen worlds. Wang et al. · arXiv 2023 · Reasoning & RL. Read the paper ↗ https://arxiv.org/abs/2305.16291 A free, interactive, animated visual explainer of Voyager: An Open-Ended Embodied Agent with Large Language Models — every exhibit computed from the real formulas, with verbatim quotes from the source. Questions - What is Voyager: An Open-Ended Embodied Agent with Large Language Models? - An agent that writes its own tools: GPT-4 proposes tasks, writes executable code against the game API, verifies it works, and archives every verified program into an ever-growing skill library — skills that persist, compound, and transfer to unseen worlds. - Who published Voyager: An Open-Ended Embodied Agent with Large Language Models, and where? - Wang et al. — arXiv 2023 arXiv:2305.16291 . - Where can I find a visual explainer of Voyager: An Open-Ended Embodied Agent with Large Language Models? - Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source. Related explainers DeepSeek-R1 /deepseek-r1 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models /chain-of-thought Training language models to follow instructions with human feedback /instructgpt Direct Preference Optimization: Your Language Model is Secretly a Reward Model /dpo DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models /deepseekmath Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters /test-time-compute Constitutional AI: Harmlessness from AI Feedback /constitutional-ai DAPO: An Open-Source LLM Reinforcement Learning System at Scale /dapo