Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Predictive Preference Learning from Human Interventions
Authors: Haoyuan Cai, Zhenghao (Mark) Peng, Bolei Zhou
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach with experiments on both autonomous driving and robotic manipulation benchmarks and demonstrate its efficiency and generality. Our theoretical analysis further shows that selecting an appropriate preference horizon L balances coverage of risky states with label correctness, thereby bounding the algorithmic optimality gap. |
| Researcher Affiliation | Academia | Haoyuan Cai, Zhenghao Peng, Bolei Zhou Department of Computer Science, University of California, Los Angeles |
| Pseudocode | Yes | We summarize our method PPL in Alg. 1. Algorithm 1 Predictive Preference Learning from Human Interventions (PPL) |
| Open Source Code | Yes | Demo and code are available at: https://metadriverse.github.io/ppl. |
| Open Datasets | Yes | We evaluate our algorithm on the Meta Drive [16] and Robosuite [49] benchmarks, using both neural experts and real human participants, showing that PPL requires fewer expert monitoring efforts and demonstrations to achieve near-optimal policies. |
| Dataset Splits | No | The paper mentions evaluating |
| Hardware Specification | Yes | The whole experiment of PPL takes only 12 minutes on a desktop computer with an Nvidia Ge Force RTX 4080 GPU. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with specific version numbers. |
| Experiment Setup | Yes | We fix H = 10 for all the interactive imitation learning baselines. In PPL, we fix β = 0.1, choose L = 4 for the Meta Drive benchmark and Table Wiping task, and set L = 6 for the Nut Assembly task. |