Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls to external tools. However, training such agents using outcome-only rewards suffers from credit-assignment ambiguity, obscuring which intermediate steps (or tool-use decisions) lead to success or failure. In this paper, we propose PORTool, an importance-aware policy-optimization algorithm that reinforces agents’ tool-use competence from outcome-level supervision while assigning reward at the step level. Specifically, PORTool generates a rewarded…