arXiv:2605.26037v1 Announce Type: new Abstract: We test the standard RLVR tool-use recipe -- GRPO on Qwen2.5-7B-Instruct -- on a deliberately minimal knowledge-graph tool API: four Freebase navigation verbs over Complex WebQuestions. Under a self-verifiable retrieval reward, the policy's tool-grounded answer rate climbs from $3.8\%$ to $9.6\%$ over 250 steps, then collapses to $0\%$ within a single 50-step window -- a \emph{peak-then-collapse} pattern replicated across four seeds. Across seven reward designs, we find four recurring failure modes: adding denser or more targeted proxy rewards sh
This research emerges as AI agent development intensifies, highlighting critical challenges in achieving robust, reliable tool use by large language models amidst a rapid push for autonomous systems.
The 'peak-then-collapse' pattern in tool-use performance reveals fundamental limitations in current AI agentic architectures, underscoring the difficulty of building truly reliable autonomous systems.
This finding indicates that simply adding proxy rewards may not solve the core issue of AI agent fragility, shifting focus towards more robust learning and safety mechanisms rather than just reward engineering.
- · AI safety researchers
- · Developers of more robust AI architectures
- · Companies prioritizing verifiable AI performance
- · Companies deploying brittle AI agents prematurely
- · Reinforcement Learning from Human Feedback (RLHF) maximalists
- · Investors expecting rapid, smooth AI agent deployment
Current AI agent development strategies face significant re-evaluation as their brittleness is empirically demonstrated.
There will be increased investment in research addressing AI agent reliability, interpretability, and 'catastrophic forgetting' or 'peak-then-collapse' phenomena.
The timeline for general-purpose, fully autonomous AI agents may be extended as fundamental reliability issues are tackled, impacting the broader AI agents narrative.
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Read at arXiv cs.CL