SkillAdam ports Adam's moment estimates to agent skill docs to fix self-evolution loops
dair_ai · x · 2026-09-11
dair-ai highlights SkillAdam, a new approach to self-evolving agent skills, and explains why such loops typically fail:
- Direction instability: effective corrections get overwritten by iteration-local feedback instead of accumulating, so the loop keeps undoing its own fixes.
- Fixed update scope: every revision changes about the same amount regardless of whether recent case-level improvements were consistent or noisy.
SkillAdam addresses both by porting Adam's two moment estimates to discrete, non-differentiable skill documents. An optimization memory records identified problems and prior solution outcomes (first-moment analogue) to stabilize the update direction, while a volatility-driven edit budget tracks history-weighted variation of recent improvements (second-moment analogue) to size each revision.
More from coding & agent
- 95% local AI + 5% GPT-6: a full basketball tracking pipeline breakdown — TheMoonMidas · 2026-09-11
- NutPrint turns your Claude Code water footprint into almonds, right in the status bar — james_mtc · 2026-09-11
- Sentry CEO: Building a game bot starts with a mini engine of the game itself — zeeg · 2026-09-11
- Astral's goal: installing CPython via uv should get you the fastest build — charliermarsh · 2026-09-11
- python-build-standalone measured fastest CPython: 10% faster than Homebrew, 18% than official Docker — charliermarsh · 2026-09-11
- As AI agents grow capable, more compute shifts from GPUs to CPUs — AccBalanced · 2026-09-11