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DecisionRank: On-Policy Distillation for Decision-Model Rerankers — arXiv:2610.42128

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.42128
License (as archived): CC0-1.0
Archived: 2026-11-03T07:25:47.620Z
SHA-256 of the archived text: e946eb6d9dbb2bcea007960bc9e38cb9536c6cf601cc8dc03e784a516928672a
Stable URL of this page: https://metraipolis.com/documento/dc_7cc0352bfc2c

How to cite

DecisionRank: On-Policy Distillation for Decision-Model Rerankers — arXiv:2610.42128 — arXiv (en), https://arxiv.org/abs/2610.42128 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7cc0352bfc2c (Metraipolis archive dc_7cc0352bfc2c, sha256 e946eb6d9dbb2bcea007960bc9e38cb9536c6cf601cc8dc03e784a516928672a)

BibTeX

@misc{dc7cc0352bfc2c,
  title = {DecisionRank: On-Policy Distillation for Decision-Model Rerankers — arXiv:2610.42128},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.42128},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7cc0352bfc2c; sha256 e946eb6d9dbb2bcea007960bc9e38cb9536c6cf601cc8dc03e784a516928672a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.42128. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive decoding latency. However, transferring a generative LLM's ranking knowledge to a decision model remains challenging: the teacher expresses preferences through a ranked list of document ID sequences, while the student determines the ranking using scores from the first-token position. To bridge this gap, we introduce DecisionRank, a decision model trained through item-space on-policy distillation (OPD). Item-space OPD constructs the student's rollout by sorting its document scores into a ranked sequence. A generative teacher takes the resulting ranking list as inputs and provides conditional feedback to directly supervise those scores. Combined with offline ranking supervision, DecisionRank achieves state-of-the-art performance on both general and reasoning-intensive retrieval benchmarks such as TREC DL, BEIR, and BRIGHT. Across four benchmarks, item-space OPD consistently outperforms alternative training objectives. Even at 1.7B parameters, DecisionRank surpasses its 27B generative teacher on TREC DL19--23. At 8B, it outperforms its generative listwise counterpart while running $24.5\times$ faster on TREC DL and $16.7\times$ faster on BRIGHT.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.42128 — 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 2026-11-03T07:20:03.757Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).