The Regression Tax: Decomposing why skills help and hurt LLM agents

An ArXiv paper decomposes how learned skills in LLM agents produce both performance gains and degradation, introducing a "regression tax" to quantify this trade-off. The tax emerges when specialized training drifts general capabilities, reducing reliability in out-of-distribution tasks.

For builders training agentic systems, the regression tax imposes a measurable cost on specialization. Prioritizing fine-tuning on narrow domains without preserving general competence degrades deployment robustness. This suggests evaluation pipelines must now test for skill-induced regression alongside task accuracy. Workflows that rely on monolithic skill stacking become less viable; builders should adopt modular architectures that isolate specialized skills behind gating mechanisms, enabling selective activation. Second-order effect: infrastructure for online monitoring of drift between skill and general performance becomes cheaper to justify, as the tax is now analytically tractable.