SkillGLoW: Storing shared solving procedures beats per-task skill pools, +17.2 points with 3.6x smaller library

rohanpaul_ai · x · 2026-09-06

A new arXiv paper, SkillGLoW (Global-Local Weave), tackles how self-improving LLM agents should remember what worked. Existing approaches fail on long-horizon, heterogeneous workloads in opposite ways: a global skills document collapses into generic discipline, while a per-task skill pool inflates with entries bound to the instance that wrote them.

The authors argue the right unit of reuse is the solving procedure shared by a cluster of related tasks. SkillGLoW aggregates local skills from task executions into procedural families, compresses them into de-instantiated global priors, regenerates instance details per task instead of storing them, and uses a commit gate that only admits a prior when real execution shows it doesn't degrade the deployed library.

Key results:

Tested across four benchmarks (math reasoning, terminal automation, software repair, embodied control) and three models
+17.2 points on hard tasks over the no-skill baseline on average, with positive gains in all 12 continual-improvement runs; 18.0 with local regeneration
One prior per procedural family, making the library 3.6x more compact than a per-task pool

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SkillGLoW: Storing shared solving procedures beats per-task skill pools, +17.2 points with 3.6x smaller library

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