Turing Post explains what actually makes recursive self-improvement recursive, and where current attempts fall short
TheTuringPost · x · 2026-09-19
Turing Post breaks down recursive self-improvement (RSI): not every case of AI improving AI is truly recursive — if a model edits code but humans test the result, the loop repeats without changing. Real recursion emerges as more parts of that loop become editable by AI.
Key points:
- The first layer is AI improving models/algorithms: editing code, kernels, or training methods, with experiments referenced from AI4AI-Bench
- A companion piece, The Missing Pieces in Recursive Self-Improvement, reports that more experience, human guidance, and 8x compute all failed to make agents rethink strategy, and analyzes how Metan and Recuris approach the gap differently — and where each stops short
- Also includes a guide on Test-Time Training: models that keep learning during inference
A conceptual primer for engineers and researchers on the current state of RSI.
More from AGI Musings
- "RL is the whole cake, not the cherry": follow-up on pretraining vs reinforcement learning — maksym_andr · 2026-09-19
- RL isn't a cherry on the cake: pretraining is just initialization for RL — maksym_andr · 2026-09-19
- Perplexity CEO Aravind Srinivas: What would you do with 10,000 agents? — rohanpaul_ai · 2026-09-19
- Ex-Apple engineer recalls China's 996 workers napping 2 hours at desk, less productive than UK 9-to-5 — alexvoica · 2026-09-19
- Opinion: Normalize Treating Frontier AI Community as Fringe Activists — norcalnatv · 2026-09-19
- Seroter's reading list: Zed's Delta kills PRs, agents pick the infra — rseroter · 2026-09-19