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Aligning Language Models with General Preferences: An Empirical Study of Direct Nash Optimization — arXiv:2611.05373

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05373
License (as archived): CC0-1.0
Archived: 2026-11-04T18:37:39.926Z
SHA-256 of the archived text: 6e0841a6698ea6e2bbfdf784f19f781130ddfa5658acbb1d567bd74a3e6fe8fd
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How to cite

Aligning Language Models with General Preferences: An Empirical Study of Direct Nash Optimization — arXiv:2611.05373 — arXiv (en), https://arxiv.org/abs/2611.05373 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4cedff2d6de4 (Metraipolis archive dc_4cedff2d6de4, sha256 6e0841a6698ea6e2bbfdf784f19f781130ddfa5658acbb1d567bd74a3e6fe8fd)

BibTeX

@misc{dc4cedff2d6de4,
  title = {Aligning Language Models with General Preferences: An Empirical Study of Direct Nash Optimization — arXiv:2611.05373},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05373},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4cedff2d6de4; sha256 6e0841a6698ea6e2bbfdf784f19f781130ddfa5658acbb1d567bd74a3e6fe8fd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05373. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Direct Nash Optimization (DNO) aligns a language model to a general preference function by approximating the Nash equilibrium of a two-player preference game. Its practical variant, DNO-Prct, iterates on-policy sampling, LLM-based rating of the samples together with a teacher's gold response, large-margin preference-pair selection and a DPO-style contrastive update. We re-implement DNO-Prct independently and study it on two open-weight instruct models, Llama 3.1 8B Instruct and Mistral 7B Instruct v0.1, with GPT-4o or a larger open-weight model of the same family as the annotator. On AlpacaEval 2.0 with GPT-4o as judge, three iterations raise the length-controlled (LC) win rate against GPT-4o from 31.7% to 45.3% for Llama and from 6.8% to 18.4% for Mistral, and the tuned 8B model scores above Llama 3.1 70B Instruct on every reported win rate. With same-family open-weight annotators, the runs retain 62% (Llama) and 82% (Mistral) of the LC gain achieved with GPT-4o as annotator, so the rating step does not require a proprietary model. In a separate ablation configuration, variants that remove the gold responses, remove the data look-back, or accept only teacher-preferred pairs score 6 to 10 LC points below the full procedure, whereas varying the number of on-policy samples per prompt (1 to 7) moves the LC win rate by at most 2.1 points, and a margin sweep (1 to 5) run in its own pipeline by at most 2.4 points. We also report the cost of the pipeline and where the practical algorithm departs from the theory it approximates. All results are single runs, and GPT-4o is both the teacher and the evaluation judge; we discuss what this implies for the reported gains.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2611.05373 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:32:37.500Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).