RSIGame: Autonomous Agentic Game Development with Recursive Self-improvement

Play. Verify. Evolve.

"A poem is never finished, only abandoned." — Paul Valéry

Neither is a generated game. RSIGame keeps developing what it creates — autonomously exploring, improving, and verifying the game until progress saturates, then preserving the best version or opening a new stage of evolution through high-level guidance.

📰 News

• 2026.10 🔥 RSIGame is on Hugging Face Daily Papers (#10 on October 1, 2026) — and counting. Thank you for the support!

https://github.com/user-attachments/assets/51b2c303-0eb3-406f-8b02-b53dfa715683

Abstract

Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a playable version remains challenging. Naive iterative refinement can easily overfit a small set of test cases, producing fragile games with unresolved bugs, missing behaviors, and poor generalization to broader player interactions.

We introduce RSIGame, an autonomous agentic game development framework with recursive self-improvement. RSIGame organizes development into complementary local and global loops. Concretely, a local explore-diagnose-improve loop broadly explores the executable game, diagnoses and prioritizes discovered issues, and performs evidence-grounded revision, where an evolving checklist continually accumulates new testing and improvement guidance. A global loop tracks overall quality, preserves the best checkpoint, and detects saturation or regression over long-horizon development.

Beyond test-time improvement, RSIGame further internalizes successful development experience into the generator through training. Across 140 GameCraft-Bench tasks, two game engines, and five generators, RSIGame consistently improves game quality under matched development budgets. Notably, experience internalization enables Qwen3.8-27B to reach 61.38 on Godot and 58.53 on Phaser, exceeding GPT-5.5 one-shot scores while reducing Qwen's generation tokens by 11 times.

What development does

Left is the initial generation, right is what RSIGame delivers. Same game, same scripted inputs, replayed on both builds.

How it works

The inner loop, once per round:

The outer loop, across rounds. A Global Quality Monitor scores progress, holds the globally best checkpoint, and detects saturation — three consecutive checkpoints where nothing beat the held build. Saturation is not the end: sparse high-level guidance, from a person or a stronger model, opens the next stage.

Installation

Requires Linux (or macOS): the loop runs its agents in process groups and cleans them up with os.killpg, which Windows does not have.

git clone https://github.com/WenyiWU0111/RSIGame.git && cd RSIGame
pip install -e .

GameCraft-Bench supplies the tasks, rubrics and replay harness. It is a separate checkout, patched by the files in patches/ (see patches/README.txt) and installed into the same environment:

git clone https://github.com/FreedomIntelligence/gamecraft-bench.git ../gamecraft-bench
git -C ../gamecraft-bench checkout a43347534374df9a0c1a6c001aa9380862783f6d
git -C ../gamecraft-bench apply ../RSIGame/patches/gamecraft-bench_rsigame_scoring.patch
git -C ../gamecraft-bench apply ../RSIGame/patches/gamecraft-bench_openrouter_provider_pin.patch
pip install -e ../gamecraft-bench

The repair agent is a Node script in OpenGame-RSIGame, our fork of OpenGame. Both engines need it. Upstream OpenGame does not have agent_repair.ts or the changes it depends on. It needs Node 20+:

git clone https://github.com/WenyiWU0111/OpenGame-RSIGame.git ../OpenGame-RSIGame
cd ../OpenGame-RSIGame && npm install && npm run build && cd -

Godot 4.6.2 for the Godot line, and the replay tools GameCraft-Bench drives:

sudo apt-get install -y --no-install-recommends xvfb xdotool ffmpeg \
    libxcursor1 libxinerama1 libxrandr2 libxi6 libgl1 libegl1
# Godot: https://github.com/godotengine/godot/releases/tag/4.6.2-stable

Quick start

cp .env.example .env            # a model API key, plus the image-generation keys for art rounds
$EDITOR configs/default.toml    # [paths]: bench, bench_godot_bin, opengame_agent_test

rsigame config                  # what a run would actually use, resolved
rsigame check                   # paths, keys and the repair agent, before anything runs

A run develops a base game from a corpus directory with one sub-directory per task. The base games behind Table 1 are on Hugging Face. For example, the GPT-5.5 Godot bases are in t1/gpt55/base/games.tar.gz:

huggingface-cli download RSIGame/RSIGame-TableArtifacts t1/gpt55/base/games.tar.gz \
    --repo-type dataset --local-dir .
mkdir -p corpus_godot_gpt55 && tar -xzf t1/gpt55/base/games.tar.gz -C corpus_godot_gpt55
rsigame -c paper_godot_gpt develop puzzle-sokoban-dungeon \
    --corpus corpus_godot_gpt55 --root runs/paper_godot_gpt

-c can go before or after the subcommand. rsigame check is worth running first: a missing bench path, key or repair agent used to surface at round 12, not at round 0.

Configuration

Three layers, each overriding the one above:

1. configs/default.toml — every knob and the default the code itself uses
2. -c configs/experiment/<name>.toml — what one experiment changes
3. environment variables — a temporary override mid-run

Every variable this project owns is RSIGAME_<SECTION>_<KEY>, matching [section] key in the toml exactly. Secrets are read from .env only, never from a toml.

Stopping early

The monitor decides which build is the best seen so far, and the saturation stop decides when development has stopped paying: K cons