Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning

Abstract:Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-based methods that progressively expand the horizon outperform fixed-horizon alternatives. However, existing schedules are open-loop: they monotonically increase the horizon until a manually specified maximum, with no mechanism to detect when further expansion stops helping. We propose the effective interaction frontier hypothesis: a dynamic boundary beyond which additional interactions yield diminishing returns while cost grows linearly. We then introduce Elastic Horizon, a closed-loop controller that tracks this boundary via the 90th percentile of successful trajectory lengths. On AppWorld and BFCL, fixed-horizon sweeps reveal clear saturation plateaus; Elastic Horizon stabilizes the horizon inside the saturation band from both under- and over-capacity initializations, attains the best success rates across 7B and 14B backbones, and saves up to 25% of per-step trajectory tokens. Our work shifts the paradigm from how to scale interaction horizons to when to stop scaling.

17 pages, 6 figures, 12 tables. Accepted to EMNLP 2026

Artificial Intelligence (cs.AI)

arXiv:2609.07247 [cs.AI]

(or arXiv:2609.07247v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.07247 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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