Fine-grained credit assignment at individual decision points in agent sequences, rather than at coarse tool-call boundaries, significantly improves learning efficiency and tool-use performance in agentic RL systems.
APPO improves how AI agents learn to use tools by identifying the most important decision points in their reasoning sequences and assigning credit more precisely.