Self-improving AI / Voyager
Voyager
“We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention.”
Voyager is one of the projects on the self-improving AI map. Below is what it changes about itself, whether the change lasts, and whether it improves the improver, each with the line from its own source.
Where it sits
Columns: what it changes, from the surface to the deepest change. Rows: whether the change outlasts the run. A number is how many projects sit in that square. See it on the 3D map
- What it changes
- Tools, harness, its own code“Voyager incrementally builds a skill library by storing the action programs that help solve a task successfully.”
- Does the change last
- Saved for later runs“Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch”
- Does it improve the improver
- improves the task agent; no evidence the improver got better“Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning.”
- Who sets the goals
- people set the aim, the system picks tasks“The curriculum is generated by GPT-4 based on the overarching goal of "discovering as many diverse things as possible".”
- How it was tested
- tested on held-out tasks“Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize.”
What grows is a library of skill programs. GPT-4 is queried as a black box and not fine-tuned. The curriculum is generated by GPT-4 from an overarching goal the authors set.
Does it count as RSI?
It depends on the definition. Under the four used on What counts as RSI, by what its own source says:
- Loose (Eliezer Yudkowsky, Intelligence Explosion Microeconomics (2013)): counts
- Durable (Ren et al., self-improvement survey (2026)): counts
- Strict (Roman Yampolskiy, From Seed AI to Technological Singularity (2015)): does not count
- Autonomous (Anthropic, When AI builds itself): does not count
How it got here
Earlier work in this catalog that Voyager cites, oldest first. Under each: where that work sits on the map, then where Voyager sits (changes in bold), and what Voyager's own paper says about it. Its whole family on the citation map
2023 Reflexion uses it
Prompts, memory → Tools, own code · gone when the run ends → saved for later runs · does not improve the improver → improves the task agent
“We evaluate Voyager systematically against other LLM-based agent techniques (e.g., ReAct [29], Reflexion [30], AutoGPT [28]) in MineDojo [23], an open-source Minecraft AI framework.”
“Reflexion [30] is built on top of ReAct [29] with self-reflection to infer more intuitive future actions.”
2023 Voyager
Tools, own code · saved for later runs · improves the task agent
Cited by Promptbreeder 2023, STOP 2023, ADAS 2024, Gödel Agent 2024, SICA 2025, Darwin Gödel Machine 2025, Hyperagents 2026, Ren, self-improvement 2026, Weco AIDE² 2026, Recursive Harness Self-Improvement 2026, Fibona Agent 2026, Self-HPO 2026, CoEvo-RSI 2026, RSI-Opt 2026, Skill∞ 2026, VISTA 2026
In the same square
AutoTavern, Harness², RRSI (Regularized Recursive Self-Improvement), RSIV-Harness