Guide, Then Let Go: Gap-Adaptive Teacher Scheduling for Sparse-Reward Agentic RL.

arXiv:2609.37898v1 Announce Type: new Abstract: Reinforcement learning for long-horizon agents typically relies on sparse outcome-based rewards. This leads to a severe cold-start problem, as early-stage policies often fail to solve sampled tasks, leaving littl