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A nine-dimension taxonomy of RSI methods and insights from traditional deep learning to inspire new research.
Watch the walkthrough · 1 min
awesome-rsi-walkthrough.mp4
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Awesome RSI collects benchmarks, methods, and systems for Recursive Self-Improvement. We follow the loop: an agent performs tasks, learns from trajectories and feedback, updates its own state, and uses that updated state in later work. That state can be model parameters, harness code, context, memory, or skills.
113 method papers · 33 benchmark papers · 3 systems
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An introduction to RSI
Understanding RSI — an illustrated guide, available in English and Chinese.
A method to build on
Methods & Systems — filter by artifacts, topology, feedback, and more.
A way to measure improvement
Benchmarks — compare artifacts and online, offline, or hybrid protocols.
Connections between papers
Citation graphs — switch between methods and benchmarks, search for a node, zoom, and pan.
Each list in this README is organized along one dimension: RSI artifact for methods and systems, and RSI mode for benchmarks. For more ways to explore the collection, visit the Awesome RSI website, where you can compare additional dimensions and combine filters to find work that matches your interests.
What does the agent improve? The tree below follows the RSI artifact dimension.
The tree shows examples from the collection. The lists below use the website's existing labels; a work can appear under multiple artifacts. Inclusion covers both explicit self-modification and experience-driven improvement during long-running tasks.
Grouped by RSI artifact and ordered by first publication or project release date, newest first. Conference labels reflect the venue recorded in the collection; projects link to their repositories.
Model parameters
The student's model parameters are updated to improve task performance. This includes a teacher using student performance feedback to refine successive training rounds, even when each round restarts from the initial model, as well as the updated student continuing to learn from later tasks.
Prompts or working context are updated. Observations, feedback, and experience can accumulate or be reorganized during a continuous run to guide later actions or tasks.
Grouped by RSI mode, newest first. Benchmarks that support multiple protocols appear in each relevant group. Artifact labels describe what evolves in the evaluated workflow.
Choose a collection: Benchmark papers or Methods & Systems.
Narrow the question: select multiple tags within a row, then combine dimensions across rows. For example, look for methods that update Skill, run Online, and use Environment feedback.
Read and compare: search titles, sort by recency or citations, and expand abstracts and taxonomy profiles.
Follow the connections: open Citation graph, choose methods or benchmarks, and search for a paper. Drag and zoom to explore its neighborhood.
The site supports English and Chinese, with the language switch in the upper-right corner.
Citation
If this collection is useful in your work, please cite it:
@misc{awesomersi2026,
title = {Awesome RSI: A Curated Collection of Recursive Self-Improvement Benchmarks and Methods},
author = {Prism-Shadow},
year = {2026},
howpublished = {\url{https://github.com/Prism-Shadow/awesome-rsi}},
}
WeChat Group
Scan the QR code to join the Awesome RSI discussion group.