Framework trains agents to write and refine their own executable skills
A new framework called WER addresses a critical gap in agent-based AI systems: while models can follow expert-written procedures, they struggle to author and improve their own. The research demonstrates that agent-generated skills underperform by 8-11 points compared to baseline approaches, revealing that execution and authorship require distinct training regimes. WER decouples skill writing from execution by training a separate optimizer module against frozen agents, using programmatic verification to score outcomes and extract learning signals. This separation of concerns could reshape how tool-using agents develop and refine their own reasoning strategies, moving beyond static procedural templates toward adaptive, self-improving systems.
Modelwire context
ExplainerWER's core insight is that skill authorship and skill execution are trainable separately, not as a joint capability. The 8-11 point underperformance gap suggests that asking a frozen agent to both write and follow procedures creates a bottleneck that programmatic feedback can bypass.
This directly addresses a fragility concern raised in recent work on self-improving agents. The August study on memory-based self-improvement found high variance and task-order sensitivity, suggesting online learning loops can amplify noise. WER sidesteps that risk by freezing the executor and training an optimizer module in isolation against deterministic verification signals. Unlike Chain-of-Experience, which accumulates feedback during inference within a single session, WER treats skill generation as a distinct offline phase, reducing the real-time adaptation surface that introduces instability. The separation of concerns here is a structural answer to the robustness problem those earlier papers flagged.
If WER-optimized skills maintain their performance gain when deployed on out-of-distribution tasks not seen during optimizer training, that confirms the approach generalizes beyond the verification signal it was tuned on. If the performance gap shrinks to under 3 points, it suggests the authorship bottleneck was overstated and simpler joint training might recover most gains.
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MentionsWER · Skill Optimizer
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Write, Execute, Refine: From Skill Followers to Skill Optimizers via Reinforcement Learning from Execution Feedback”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.