Recoverable Identifier

arXiv:2605.15417 · detector doi_compliance · incontrovertible · 2026-05-19 16:04:40.928097+00:00

Paper page Integrity report arXiv Try DOI

Evidence text

PMLR, 2020. D. Go, T. Korbak, G. Kruszewski, J. Rozen, N. Ryu, and M. Dymetman. Aligning language models with prefer- ences through f-divergence minimization.arXiv preprint arXiv:2302.08215, 2023. J. Han, M. Jiang, Y . Song, S. Ermon, and M. Xu. f-po: Generalizing preference optimization with f-divergence minimization.arXiv preprint arXiv:2410.21662, 2024. D. Hendrycks, C. Burns, S. Kadavath, A. Arora, S. Basart, E. Tang, D. Song, and J. Steinhardt. Measuring mathemat- ical problem solving with the math dataset. InThirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). A. Huang, W. Zhan, T. Xie, J. D. Lee, W. Sun, A. Kr- ishnamurthy, and D. J. Foster. Correcting the mythos of kl-regularization: Direct alignment without overopti- mization via chi-squared preference optimization. InThe Thirteenth International Conference on Learning Repre- sentations. P. Intellect. Prime-rl, 2025. URL https://github. com/PrimeIntellect-ai/prime-rl. Q. Jin, B. Dhingra, Z. Liu, W. Cohen, and X. Lu. Pubmedqa: A dataset for biomedical research question answering. In Proceedings of the 2019 Conference on Empirical Meth- ods in Natural Language Processing and the 9th Interna- tional Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2567–2577, 2019. L. Ke, S. Choudhury, M. Barnes, W. Sun, G. Lee, and S. Srinivasa. Imitation learning as f-divergence mini- mization. InInternational workshop on the algorithmic foundations of robotics, 

Evidence payload

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  "reconstructed_doi": "10.64434/tml.20251026.https://thinkingmachines.ai/blog/on-policy-distillation.N.Malkin",
  "ref_index": 1,
  "resolved_title": null,
  "verdict_class": "incontrovertible"
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