# Offline Preference-Based Trajectory Evaluation

arXiv: https://arxiv.org/abs/2606.17541 · HTML: https://mlsanity.com/paper/2606.17541/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 30 of 30)

- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Dynabench: Rethinking Benchmarking in NLP](https://mlsanity.com/paper/2104.14337/) (2021)
- [AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents](https://mlsanity.com/paper/2401.13178/) (2024)
- [TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks](https://mlsanity.com/paper/2412.14161/) (2024)
- [tinyBenchmarks: evaluating LLMs with fewer examples](https://mlsanity.com/paper/2402.14992/) (2024)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Nash Learning from Human Feedback](https://mlsanity.com/paper/2312.00886/) (2023)
- [A Minimaximalist Approach to Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2401.04056/) (2024)
- [Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation](https://mlsanity.com/paper/2510.11977/) (2025)
- [Active Testing: Sample-Efficient Model Evaluation](https://mlsanity.com/paper/2103.05331/) (2021)
- [Mapping global dynamics of benchmark creation and saturation in artificial intelligence](https://mlsanity.com/paper/2203.04592/) (2022)
- [Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge](https://mlsanity.com/paper/2502.00561/) (2025)
- [Comparing Test Sets with Item Response Theory](https://mlsanity.com/paper/2106.00840/) (2021)
- [Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking](https://mlsanity.com/paper/2106.06052/) (2021)
- [Active Evaluation Acquisition for Efficient LLM Benchmarking](https://mlsanity.com/paper/2410.05952/) (2024)
- [When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation](https://mlsanity.com/paper/2602.16763/) (2026)
- [Reliable and Efficient Amortized Model-based Evaluation](https://mlsanity.com/paper/2503.13335/) (2025)
- [Better than Average: Paired Evaluation of NLP Systems](https://mlsanity.com/paper/2110.10746/) (2021)
- [Label-Efficient Model Selection for Text Generation](https://mlsanity.com/paper/2402.07891/) (2024)
- [AcTracer: Active Testing of Large Language Model via Multi-Stage Sampling](https://mlsanity.com/paper/2408.03573/) (2024)
- [Comparison requires valid measurement: Rethinking attack success rate comparisons in AI red teaming](https://mlsanity.com/paper/2601.18076/) (2026)
- [Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons](https://mlsanity.com/paper/2203.06063/) (2022)
- [Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor](https://mlsanity.com/paper/2506.14652/) (2025)
- [SimBA: Simplifying Benchmark Analysis Using Performance Matrices Alone](https://mlsanity.com/paper/2510.17998/) (2025)
- [Offline Retrieval Evaluation Without Evaluation Metrics](https://mlsanity.com/paper/2204.11400/) (2022)
- [Recall, Robustness, and Lexicographic Evaluation](https://mlsanity.com/paper/2302.11370/) (2023)
- [How Much Freedom Does An Effectiveness Metric Really Have?](https://mlsanity.com/paper/2309.09477/) (2023)

## Built on by (most cited first, 2 of 2)

- [Multi-Agent Transactive Memory](https://mlsanity.com/paper/2606.19911/) (2026)
- [Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfaces](https://mlsanity.com/paper/2610.16191/) (2026)

## Abstract (arXiv)

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.