arXiv:2605.23904v1 Announce Type: new Abstract: Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should instead be trained as the external state of a frozen agent, with the same discipline that makes weight-space optimization reproducible. SkillOpt is, to our knowledge, the first systematic controllable text-space optimizer for agent skills: a separate optimizer model turns
The proliferation of AI agents has highlighted limitations in current skill development methods, prompting a search for more robust and scalable solutions.
This development proposes a systematic approach to agent skill optimization, potentially accelerating the development of more capable and reliable AI agents.
Agent skills can now be trained and optimized with a discipline akin to weight-space optimization in deep learning, moving beyond hand-crafted or loosely evolved capabilities.
- · AI agent developers
- · Companies adopting AI agents
- · AI research institutions
- · Developers relying on manual skill engineering
- · Frameworks lacking robust skill optimization
More sophisticated and reliable AI agents become widely deployable in various sectors.
Increased automation across white-collar workflows leads to significant productivity gains and shifts in labor markets.
The complexity and autonomy of AI systems grow significantly, demanding new regulatory frameworks and safety protocols.
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Read at arXiv cs.AI