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
- Karpathy shares CLAUDE.md rulebook to stop AI coding agents from piling up tech debt — mdancho84 · 2026-10-26
- Rethinking dependencies in the agent era: drop libs that save 20 lines of code — gethackteam · 2026-10-26
- RustGIS hits 2,000 GitHub stars in two weeks with agent-first architecture — giswqs · 2026-10-26
- Vercel Ships 12 New Products at Its 2026 Conference as It Races Rivals for the Agent Entry Point — FinanceYF5 · 2026-10-26
- Build an AI agent from scratch in under 50 lines of pure Python: ReAct loop tutorial — adnan_hashmi · 2026-10-26
- Dense instance segmentation via Muse Wonder + SAM 3.1 on Meta's Model API — nikhilaravi · 2026-10-26