AINeutralarXiv – CS AI · May 296/10
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Reasoning and Tool-use Compete in Agentic RL:From Quantifying Interference to Disentangled Tuning
Researchers demonstrate that jointly training language models for both reasoning and tool-use in agentic RL creates measurable performance interference. They introduce DART, a framework that decouples these capabilities through separate low-rank adaptation modules, achieving superior results across thirteen benchmarks and approaching theoretical performance limits.