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News/Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization
arxiv
PublishedJune 2, 2026 at 4:00 AM
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Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization

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arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2510.05342v2 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) has emerged as a simple and effective method for aligning large language models. However, its reliance on a fixed temperature parameter leads to suboptimal training on diverse preference data, causing over

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Discussion
Mentioned models
04
  • 01
    Direct Preference Optimization (DPO)
  • 02
    IPO
  • 03
    $\beta$-DPO
  • 04
    Margin-Adaptive Direct Preference Optimization (MADPO)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#optimization#language-models#preference-learning

No replies yet. Be first.

Mentioned models
04
  • 01
    Direct Preference Optimization (DPO)
  • 02
    IPO
  • 03
    $\beta$-DPO
  • 04
    Margin-Adaptive Direct Preference Optimization (MADPO)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-learning#optimization#language-models#preference-learning

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Originally published on arxiv ↗
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