ToolRLA: Fine-Grained Reward Decomposition for Tool-Integrated Reinforcement Learning Alignment in Domain-Specific Agents

Researchers developed ToolRLA, a three-stage reinforcement learning pipeline that significantly improves AI agents' ability to use external tools and APIs for domain-specific tasks. The system achieved 47% higher task completion rates and 93% lower regulatory violations when deployed in a real-world financial advisory copilot serving 80+ advisors with 1,200+ daily queries.