A new approach to reinforcement learning, called delayed per-step reward attribution, has been developed for training language model agents in multi-agent environments, achieving competitive results against larger proprietary models. This method, evaluated at NeurIPS 2025, demonstrates the potential for open-source models to excel in strategic interactions, suggesting a shift in the landscape of AI training methodologies.