@inproceedings{yang-etal-2026-topocurate,
title = "{T}opo{C}urate: Modeling Interaction Topology for Tool-Use Agent Training",
author = "Yang, Jinluan and
Liu, Yuxin and
Chen, Zhengyu and
Han, Chengcheng and
Sun, Yueqing and
Song, Xierui and
Su, Hui and
Cai, Xunliang and
Wu, Fei and
Kuang, Kun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1969/",
pages = "41986--42006",
ISBN = "979-8-89176-462-0",
abstract = "Training tool-use agents typically relies on outcome-based filtering: Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks. However, this paradigm ignores interaction dynamics{---}successful trajectories may lack error recovery or exhibit redundancy, while pass rates fail to distinguish structurally informative tasks from trivial ones. To bridge this gap, we propose \textbf{TopoCurate}, an interaction-aware framework that shifts the paradigm from linear outcome filtering to topological interaction modeling. By projecting multi-trial rollouts into a unified semantic quotient topology within the same task, we construct a structured manifold that explicitly captures the divergence between success and failure pathways across tool invocations and environmental responses. Leveraging this representation, we introduce a dual-selection mechanism: for SFT, we prioritize trajectories demonstrating reflective recovery, semantic efficiency, and strategic diversity to mitigate covariate shift and mode collapse; for RL, we select tasks with high error branch ratios and strategic heterogeneity, maximizing gradient Signal-to-Noise Ratio to address vanishing signals in sparse-reward settings. Evaluations on BFCLv3 and Tau2 Bench show that TopoCurate achieves consistent gains of 4.2{\%} (SFT) and 6.9{\%} (RL) over state-of-the-art baselines."
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<abstract>Training tool-use agents typically relies on outcome-based filtering: Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks. However, this paradigm ignores interaction dynamics—successful trajectories may lack error recovery or exhibit redundancy, while pass rates fail to distinguish structurally informative tasks from trivial ones. To bridge this gap, we propose TopoCurate, an interaction-aware framework that shifts the paradigm from linear outcome filtering to topological interaction modeling. By projecting multi-trial rollouts into a unified semantic quotient topology within the same task, we construct a structured manifold that explicitly captures the divergence between success and failure pathways across tool invocations and environmental responses. Leveraging this representation, we introduce a dual-selection mechanism: for SFT, we prioritize trajectories demonstrating reflective recovery, semantic efficiency, and strategic diversity to mitigate covariate shift and mode collapse; for RL, we select tasks with high error branch ratios and strategic heterogeneity, maximizing gradient Signal-to-Noise Ratio to address vanishing signals in sparse-reward settings. Evaluations on BFCLv3 and Tau2 Bench show that TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T TopoCurate: Modeling Interaction Topology for Tool-Use Agent Training
%A Yang, Jinluan
%A Liu, Yuxin
%A Chen, Zhengyu
%A Han, Chengcheng
%A Sun, Yueqing
%A Song, Xierui
%A Su, Hui
%A Cai, Xunliang
%A Wu, Fei
%A Kuang, Kun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yang-etal-2026-topocurate
%X Training tool-use agents typically relies on outcome-based filtering: Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks. However, this paradigm ignores interaction dynamics—successful trajectories may lack error recovery or exhibit redundancy, while pass rates fail to distinguish structurally informative tasks from trivial ones. To bridge this gap, we propose TopoCurate, an interaction-aware framework that shifts the paradigm from linear outcome filtering to topological interaction modeling. By projecting multi-trial rollouts into a unified semantic quotient topology within the same task, we construct a structured manifold that explicitly captures the divergence between success and failure pathways across tool invocations and environmental responses. Leveraging this representation, we introduce a dual-selection mechanism: for SFT, we prioritize trajectories demonstrating reflective recovery, semantic efficiency, and strategic diversity to mitigate covariate shift and mode collapse; for RL, we select tasks with high error branch ratios and strategic heterogeneity, maximizing gradient Signal-to-Noise Ratio to address vanishing signals in sparse-reward settings. Evaluations on BFCLv3 and Tau2 Bench show that TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines.
%U https://aclanthology.org/2026.findings-emnlp.1969/
%P 41986-42006
Markdown (Informal)
[TopoCurate: Modeling Interaction Topology for Tool-Use Agent Training](https://aclanthology.org/2026.findings-emnlp.1969/) (Yang et al., Findings 2026)
ACL
- Jinluan Yang, Yuxin Liu, Zhengyu Chen, Chengcheng Han, Yueqing Sun, Xierui Song, Hui Su, Xunliang Cai, Fei Wu, and Kun Kuang. 2026. TopoCurate: Modeling Interaction Topology for Tool-Use Agent Training. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 41986–42006, Budapest, Hungary. Association for Computational Linguistics.