AINeutralarXiv – CS AI · May 46/10
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PORTool: Importance-Aware Policy Optimization with Rewarded Tree for Multi-Tool-Integrated Reasoning
PORTool is a new policy-optimization algorithm that improves how AI agents learn to use external tools by solving the credit-assignment problem in multi-step reasoning tasks. The method uses a rewarded tree structure to assign rewards at individual steps rather than only at outcomes, enabling agents to achieve higher accuracy while reducing unnecessary tool calls.