Offline Preference-Based Trajectory Evaluation
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Computer Science > Machine Learning
Title:Offline Preference-Based Trajectory Evaluation
Abstract:Offline evaluation of agentic systems often collapses trajectories to terminal success, discarding information about partial progress and inducing widespread ties, creating substantial statistical inefficiency by reducing effective sample size and weakening the ability to distinguish systems. We propose preference-based trajectory evaluation, which compares trajectories directly through temporal preferences over progress and time-to-return profiles. We find that, across diverse agentic and interactive benchmarks, standard success-based metrics produce tied comparisons on roughly 75% of instances, whereas trajectory-aware preferences reduce ties to roughly 35%, improving discriminative power, ranking stability, and data efficiency. Our results suggest that benchmark saturation, often attributed to poor data collection or problem difficulty, may also be explained by the choice of evaluation measure.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.17541 [cs.LG] |
| (or arXiv:2606.17541v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17541
arXiv-issued DOI via DataCite (pending registration)
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