Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe

arXiv:2603.21383v1 Announce Type: new Abstract: Post-training for long-horizon agentic tasks has a tension between compute efficiency and generalization. While supervised fine-tuning (SFT) is compute efficient, it often suffers from out-of-domain (OOD) degradation.