arXiv:2605.27788v1 Announce Type: new Abstract: Humans know when to reach for help e.g. $347 \times 28$ warrants a calculator while $2+2$ does not. Language models do not. Prompt-based approaches can instruct a model when to invoke tools, but this scaffolding does not teach it to recognize the boundary of its own knowledge. RL approaches that assign a single outcome reward to the whole trajectory fare no better: trajectory-level credit cannot isolate which tool call in a successful episode actually helped, nor penalize unnecessary calls. We propose \textbf{CARL} (\textbf{C}ompetence-\textbf{A}
This research addresses a fundamental limitation in current LLM capabilities, as models evolve rapidly beyond basic understanding to complex reasoning and external tool integration.
Overcoming the 'knowing when to ask' problem is critical for building truly autonomous and efficient AI agents, improving their reliability and reducing unnecessary compute cycles.
This research introduces a novel credit assignment mechanism that allows large language models to more effectively learn when to invoke external tools, moving beyond simple prompt-based instructions or crude trajectory-level rewards.
- · AI agents developers
- · Companies deploying LLM-powered systems
- · AI research institutions
- · Inefficient LLM-integration methods
- · Systems requiring extensive human oversight for tool use
LLMs will become more efficient and robust in utilizing external tools, enhancing their problem-solving capabilities.
The development of more sophisticated and autonomous AI agents will accelerate, enabling them to tackle more complex tasks with less human intervention.
This could lead to a broader adoption of AI agents across various industries, collapsing workflows and disrupting traditional SaaS models.
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Read at arXiv cs.LG