@inproceedings{lu-etal-2026-beyond,
title = "Beyond the Context Window: Scaling Agentic {RL} via End-to-end Optimized Context Compression",
author = "Lu, Miao and
Sun, Weiwei and
Du, Weihua and
Ling, Zhan and
Yao, Xuesong and
Liu, Kang and
Chen, Jiecao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.966/",
pages = "21074--21125",
ISBN = "979-8-89176-390-6",
abstract = "We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing multi-turn RL pipelines suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. In this work, to address these challenges, we introduce \textit{summarization-based context management to training}. In specific, it periodically compresses the tool using history by LLM-generated summaries that retain task-relevant information to keep a compact context while enabling the agent to scale beyond the fixed context window. Building on this formulation, we derive a policy gradient representation that seamlessly enables standard LLM RL infrastructures to optimize both tool-use behaviors as well as summarization strategies in an end-to-end fashion. We instantiate this framework with SUmmarization augmented Policy Optimization (SUPO), an LLM RL algorithm that enables long-horizon training beyond a fixed context limit. Experiments on interactive function calling and searching tasks demonstrate that SUPO significantly improves the success rate while maintaining the same or even lower working context length compared to baselines. We also demonstrate that for complex searching tasks SUPO can further improve the evaluation performance when scaling test-time maximum round of summarization beyond that of training time."
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<abstract>We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing multi-turn RL pipelines suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. In this work, to address these challenges, we introduce summarization-based context management to training. In specific, it periodically compresses the tool using history by LLM-generated summaries that retain task-relevant information to keep a compact context while enabling the agent to scale beyond the fixed context window. Building on this formulation, we derive a policy gradient representation that seamlessly enables standard LLM RL infrastructures to optimize both tool-use behaviors as well as summarization strategies in an end-to-end fashion. We instantiate this framework with SUmmarization augmented Policy Optimization (SUPO), an LLM RL algorithm that enables long-horizon training beyond a fixed context limit. Experiments on interactive function calling and searching tasks demonstrate that SUPO significantly improves the success rate while maintaining the same or even lower working context length compared to baselines. We also demonstrate that for complex searching tasks SUPO can further improve the evaluation performance when scaling test-time maximum round of summarization beyond that of training time.</abstract>
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%0 Conference Proceedings
%T Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression
%A Lu, Miao
%A Sun, Weiwei
%A Du, Weihua
%A Ling, Zhan
%A Yao, Xuesong
%A Liu, Kang
%A Chen, Jiecao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F lu-etal-2026-beyond
%X We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing multi-turn RL pipelines suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. In this work, to address these challenges, we introduce summarization-based context management to training. In specific, it periodically compresses the tool using history by LLM-generated summaries that retain task-relevant information to keep a compact context while enabling the agent to scale beyond the fixed context window. Building on this formulation, we derive a policy gradient representation that seamlessly enables standard LLM RL infrastructures to optimize both tool-use behaviors as well as summarization strategies in an end-to-end fashion. We instantiate this framework with SUmmarization augmented Policy Optimization (SUPO), an LLM RL algorithm that enables long-horizon training beyond a fixed context limit. Experiments on interactive function calling and searching tasks demonstrate that SUPO significantly improves the success rate while maintaining the same or even lower working context length compared to baselines. We also demonstrate that for complex searching tasks SUPO can further improve the evaluation performance when scaling test-time maximum round of summarization beyond that of training time.
%U https://aclanthology.org/2026.acl-long.966/
%P 21074-21125
Markdown (Informal)
[Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression](https://aclanthology.org/2026.acl-long.966/) (Lu et al., ACL 2026)
ACL
- Miao Lu, Weiwei Sun, Weihua Du, Zhan Ling, Xuesong Yao, Kang Liu, and Jiecao Chen. 2026. Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21074–21125, San Diego, California, United States. Association for Computational Linguistics.