Watch the walkthrough · 1 min
https://github.com/user-attachments/assets/70f488af-925c-4b93-ba03-5c84711b8dbf
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.
100 method papers · 31 benchmark papers · 3 systems
| Looking for… | Start with… |
|---|---|
| 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. |
| More background | Books, courses, and learning materials. |
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.
Model parameters · Harness code · Context · Memory · Skill · Other artifacts · Benchmarks
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.
Compare all dimensions on the website →
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.
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.
Executable agent, harness, control-flow, self-improvement, or tool code is modified.
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.
Information or experience is stored, updated, and retrieved across steps, trajectories, or tasks.
Reusable strategies, procedures, workflows, or skill resources are created and revised.
Other artifacts evolve, such as data strategies, experiment configurations, or task solutions.
| Paper or project | Publication |
|---|---|
| Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement | arXiv preprint |
| EnvHarness: Awakening Static Worlds for Agent Learning | arXiv preprint |
Compare benchmark dimensions on the website →
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.
Online · Offline · Offline → Online
Experience accumulates during the task stream or repeated interaction, and later work can use it.
Evolution precedes a separate held-out evaluation.
An artifact is built offline and continues to evolve during online use.
| Benchmark paper | Year | RSI artifact |
|---|---|---|
| SkillFlow: Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents | 2026 | Context · Skill |
The site supports English and Chinese, with the language switch in the upper-right corner.
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}},
}
Scan the QR code to join the Awesome RSI discussion group.
6,863 followers · starred Sep 2026
720 followers · starred Sep 2026
3,228 followers · starred Oct 2026
125 followers · starred Oct 2026
Watch the walkthrough · 1 min
https://github.com/user-attachments/assets/70f488af-925c-4b93-ba03-5c84711b8dbf
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.
100 method papers · 31 benchmark papers · 3 systems
| Looking for… | Start with… |
|---|---|
| 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. |
| More background | Books, courses, and learning materials. |
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.
Model parameters · Harness code · Context · Memory · Skill · Other artifacts · Benchmarks
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.
Compare all dimensions on the website →
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.
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.
Executable agent, harness, control-flow, self-improvement, or tool code is modified.
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.
Information or experience is stored, updated, and retrieved across steps, trajectories, or tasks.
Reusable strategies, procedures, workflows, or skill resources are created and revised.
Other artifacts evolve, such as data strategies, experiment configurations, or task solutions.
| Paper or project | Publication |
|---|---|
| Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement | arXiv preprint |
| EnvHarness: Awakening Static Worlds for Agent Learning | arXiv preprint |
Compare benchmark dimensions on the website →
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.
Online · Offline · Offline → Online
Experience accumulates during the task stream or repeated interaction, and later work can use it.
Evolution precedes a separate held-out evaluation.
An artifact is built offline and continues to evolve during online use.
| Benchmark paper | Year | RSI artifact |
|---|---|---|
| SkillFlow: Benchmarking Lifelong Skill Discovery and Evolution for Autonomous Agents | 2026 | Context · Skill |
The site supports English and Chinese, with the language switch in the upper-right corner.
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}},
}
Scan the QR code to join the Awesome RSI discussion group.
6,863 followers · starred Sep 2026
720 followers · starred Sep 2026
3,228 followers · starred Oct 2026
125 followers · starred Oct 2026