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Multi-Teacher Reasoning Distillation for LLM Recommendation with Self-Exploration — arXiv:2611.02457

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02457
License (as archived): CC0-1.0
Archived: 2026-11-03T10:28:14.922Z
SHA-256 of the archived text: b3a35bf2c336c7adc98a235745e4ed23ccec739d05a2d3f4f9c167937db3a67e
Stable URL of this page: https://metraipolis.com/documento/dc_dd63bf44d8fb

How to cite

Multi-Teacher Reasoning Distillation for LLM Recommendation with Self-Exploration — arXiv:2611.02457 — arXiv (en), https://arxiv.org/abs/2611.02457 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dd63bf44d8fb (Metraipolis archive dc_dd63bf44d8fb, sha256 b3a35bf2c336c7adc98a235745e4ed23ccec739d05a2d3f4f9c167937db3a67e)

BibTeX

@misc{dcdd63bf44d8fb,
  title = {Multi-Teacher Reasoning Distillation for LLM Recommendation with Self-Exploration — arXiv:2611.02457},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02457},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dd63bf44d8fb; sha256 b3a35bf2c336c7adc98a235745e4ed23ccec739d05a2d3f4f9c167937db3a67e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02457. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Reasoning-augmented recommender systems (RecSys) enhance user preference modeling by generating reasoning trajectories over historical interactions before making recommendations. Knowledge distillation provides a practical paradigm for transferring such reasoning capabilities from powerful large language models to lightweight RecSys. However, existing methods typically rely on a single fixed teacher, remain constrained by the teacher knowledge ceiling, and indiscriminately distill teacher-generated trajectories without assessing their actual utility for downstream recommendation. To address these limitations, we propose MUSE (Multi-Teacher Utility-guided Selection with Self-Exploration), an adaptive reasoning distillation framework that jointly improves reasoning generation and reasoning utilization. MUSE first constructs a diverse reasoning pool using multiple teacher LLMs and progressively augments it via student self-exploration, enabling the student to leverage the strengths of different teachers and gradually explore reasoning strategies beyond teacher imitation. We then develop Rank-Gain Utility Assessment (RGUA), which evaluates each candidate according to the normalized full-catalog rank improvement of the ground-truth item and identifies the most and least recommendation-beneficial trajectories. Based on these utility-aware trajectory pairs, we introduce dual-level contrastive learning objectives that guide the student generator to prefer beneficial reasoning while training the recommendation encoder to more effectively exploit recommendation-relevant preference evidence. Extensive experiments on three real-world datasets demonstrate the effectiveness and robustness of the proposed framework.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02457 — 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 2026-11-03T10:19:50.178Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).