The Regression Tax: Decomposing why skills help and hurt LLM agents
WHY IT MATTERS
An ArXiv paper analyzes how learned skills can both benefit and hinder LLM agents, introducing the concept of a 'regression tax' that explains performance trade-offs.
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.
SOURCE
ArXiv
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