Author

Nabil Omi

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#machine learning Preprint Sep 2026

JumpStart Your Policy Learning with Lessons from 160,000 Training Runs

Reliable progress in offline policy learning depends on careful reporting, well-tuned baselines, and evaluation across diverse conditions. Prior work has shown that results can be sensitive to reporting choices, hyperparameter tuning, and dataset properties, but these sources of variability have not been systematically...

Nabil Omi, Eric Bae, C. Yeung et al. · 0 citations

#artificial intelligence Review Sep 2026

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

Harbor Adapters are developed that port more than 80 benchmarks to evaluate arbitrary agents, and validated through rigorous code review and parity experiments, and Harbor-Index is introduced, a curated set of 82 difficult, diverse, and high-quality tasks spanning 29 benchmarks to support more reliable and comprehensiv...

Lin Shi, Hao-Wei Lin, Zi-Xuan Zhu et al. · 1 citation