The Experiment Testing Whether RSI Can Accelerate Itself - Zhengyao Jiang

Weco let an AI coding agent rewrite the harness around another agent for eight days: its code, prompts and tools, while the underlying language model stayed fixed. Tim Scarfe asks Weco co-founder Zhengyao Jiang what the reported gains over two years of human engineering actually demonstrate. The discussion examines AIDE 85's generated code, held-out evaluation and the difficulty of separating useful discoveries from reward hacking. Jiang explains Weco's four levels of recursive self-improvement and compares the experiment with AlphaEvolve and the Darwin Gödel Machine. The limits matter as much as the gains. Jiang explains why the experiment did not establish that the system had become a better improver. The conversation closes with open-ended search, human-designed primitives and Parameter Golf: where does the next useful idea come from when the agent is searching inside a space that people designed? --- TIMESTAMPS: 00:00:00 Eight days of self-improvement: what counts? 00:03:25 AIDE and the puzzle of useful spaghetti code 00:08:38 Four levels of recursive self-improvement 00:12:02 What AIDE 85 changed and how it was tested 00:20:04 AlphaEvolve, Darwin Gödel Machine and the RSI claim 00:26:21 Reward hacking and the limits of detection 00:33:09 Open-ended search, harness tuning and creativity 00:39:43 Parameter Golf and the limits of self-improvement --- REFERENCES: organization: [00:00:30] Weco AI https://www.weco.ai/ other: [00:00:33] AIDE²: The First Evidence of Recursive Self-Improvement https://www.weco.ai/blog/first-evidence-of-recursive-self-improvement [00:14:11] Faulty reward functions in the wild https://openai.com/index/faulty-reward-functions/ [00:29:59] The Hugging Face incident and the road ahead https://openai.com/index/hugging-face-incident-and-the-road-ahead/ tool: [00:03:29] AIDE https://github.com/WecoAI/aideml [00:04:29] MLE-bench https://github.com/openai/mle-bench [00:04:33] ALE-Bench https://github.com/SakanaAI/ALE-Bench [00:04:52] WeatherBench 2 https://github.com/google-research/weatherbench2 [00:08:18] ReAct https://react-lm.github.io/ [00:39:43] Parameter Golf https://github.com/openai/parameter-golf paper: [00:20:08] AlphaEvolve: A coding agent for scientific and algorithmic discovery https://arxiv.org/abs/2506.13131v1 [00:21:35] Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents https://arxiv.org/abs/2505.22954v3 [00:23:45] Hyperagents https://arxiv.org/abs/2603.19461v1 [00:27:01] SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents https://arxiv.org/abs/2605.21384 book: [00:33:14] Why Greatness Cannot Be Planned: The Myth of the Objective https://link.springer.com/book/10.1007/978-3-319-15524-1 --- LINKS: https://app.rescript.info/share/3a9dc6189cb539c6a05fcc4f75c101b3 PDF: https://app.rescript.info/api/public/sessions/9eda60ede2b31c92/pdf