TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training

TL;DR

TopoCurate introduces an interaction-aware framework that structures multi-trial tool-use trajectories into a semantic topology, improving training by emphasizing recovery, diversity, and strategic complexity.

Contribution

It proposes a novel topology-based projection of interaction trajectories and a dual-selection mechanism for SFT and RL, enhancing agent training effectiveness.

Findings

Achieves 4.2% improvement in SFT tasks.

Achieves 6.9% improvement in RL tasks.

Demonstrates robustness across BFCLv3 and Tau2 Bench datasets.

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. We propose \textbf{TopoCurate}, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology. By merging equivalent action-observation states, this projection transforms scattered linear trajectories into a structured manifold that explicitly captures how tool invocations and environmental responses drive the divergence between effective strategies and failure modes. 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. We will release the code and data soon for further investigations.