@inproceedings{zhang-etal-2026-elastic,
title = "Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning",
author = "Zhang, Gangyi and
Meng, Junjie and
Zhang, Letian and
Wu, Wei and
Zheng, Yang and
Wang, Dong and
Liu, Yang and
Jiang, Guanjun and
Gao, Chongming",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1693/",
pages = "38533--38549",
ISBN = "979-8-89176-459-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning
%A Zhang, Gangyi
%A Meng, Junjie
%A Zhang, Letian
%A Wu, Wei
%A Zheng, Yang
%A Wang, Dong
%A Liu, Yang
%A Jiang, Guanjun
%A Gao, Chongming
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-elastic
%X 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.
%U https://aclanthology.org/2026.emnlp-main.1693/
%P 38533-38549
Markdown (Informal)
[Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning](https://aclanthology.org/2026.emnlp-main.1693/) (Zhang et al., EMNLP 2026)
ACL
- Gangyi Zhang, Junjie Meng, Letian Zhang, Wei Wu, Yang Zheng, Dong Wang, Yang Liu, Guanjun Jiang, and Chongming Gao. 2026. Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 38533–38549, Budapest, Hungary. Association for Computational Linguistics.