Another interesting approach to self-evolve agent skills. But it's important to know that skill self-evolution loops fail in two specific wa…

Another interesting approach to self-evolve agent skills. But it's important to know that skill self-evolution loops fail in two specific ways: 1. Direction instability. Effective corrections get overwritten by iteration-local feedback instead of accumulating, so the loop keeps undoing its own fixes. 2. 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. As a functional analogue of the first moment, an optimization memory records identified problems and the outcomes of prior solution attempts, which stabilizes the update direction. As an analogue of the second moment, a volatility-driven edit budget tracks the history-weighted variation of recent case-level improvements and controls how large each revision is allowed to be. Across seven benchmarks spanning short and long-horizon tasks it reaches state of the art with more stable optimization dynamics, and it gets there in substantially fewer iterations and at lower cost than prior methods. Paper: https://academy.dair.ai/papers/skilladam-stable-and-efficient-skill-evolution-for-agents-2609.08944

Source: DAIR.AI (X) | 2026-09-10

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