Recommended read. Interaction horizon scheduling is an underexplored control problem in agentic RL This paper from the Qwen team takes a closer look at the problem. Scaling the maximum number of environment interactions per episode improves long-horizon agents, and curriculum methods that expand the horizon beat fixed-horizon training. But those schedules are open-loop. They increase monotonically to a manually specified maximum with no way to detect that expansion stopped helping. The authors propose the effective interaction frontier, a dynamic boundary past which extra interactions give diminishing returns while cost keeps growing linearly. Fixed-horizon sweeps on AppWorld and BFCL show clear saturation plateaus. Elastic Horizon is a closed-loop controller that tracks the boundary using the 90th percentile of successful trajectory lengths, a statistic already available during training. It settles inside the saturation band from both under-capacity and over-capacity starts, gets the best success rates across 7B and 14B backbones, and saves up to 25% of per-step trajectory tokens. Paper: