mlsanityData to 7 Oct 2026

Disentangling Optimization Scale from Preference Scale in DPO

arXiv 2608.27032 ↗First posted 27 Aug 2026cs.LGcited by 0

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Direct Preference Optimization (DPO) is a widely used objective for aligning language models from preference data, with the coefficient β commonly interpreted as controlling the KL constraint to a reference policy. We show that β entangles two distinct roles: it governs the effective inverse preference-noise scale and simultaneously rescales the optimization dynamics, coupling this scale with the effective step size. As a consequence, at a fixed learning rate the achieved policy deviation is non-monotone in β: it vanishes in a dead zone at small β, reaches a peak at an intermediate value, and decreases again for larger β. Moreover, standard DPO loss values are not comparable across β: runs with nearly identical loss curves can differ several-fold in KL divergence from the reference model. This entanglement obscures the role of β, increases sensitivity to hyperparameter choices, and complicates learning-rate scheduling. We propose a centered-softplus reformulation that is argmin-equivalent to DPO for β>0, while making the inverse preference-noise-scale and learning-rate effects explicit and independently tunable. The normalized centered-softplus objective also admits a continuous β→0 endpoint that reduces to a linear preference-margin objective.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 32 of its 40 references that are in the map. First posted on arXiv 27 Aug 2026.

Builds on

32 of 40 references.

Closest ancestors · 19

Same direction, newest first

  1. 2026Conditional Equivalence of DPO and RLHF: Implicit Assumption, Failure Modes, and Provable Alignment20 May
  2. 2026Direct Preference Optimization with Rating Information: Practical Algorithms and Provable Gains31 Jan
  3. 2025Explicit Preference Optimization: No Need for an Implicit Reward Model9 Jun
  4. 2025Gradient Imbalance in Direct Preference Optimization28 Feb
  5. 2024Entropy Controllable Direct Preference Optimization12 Nov
  6. 2024AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization14 Oct
  7. 2024Geometric-Averaged Preference Optimization for Soft Preference Labels10 Sep
  8. 2024β-DPO: Direct Preference Optimization with Dynamic β11 Jul
  9. 2024Bootstrapping Language Models with DPO Implicit Rewards14 Jun
  10. 2024Distributional Preference Alignment of LLMs via Optimal Transport9 Jun
  11. 2024SimPO: Simple Preference Optimization with a Reference-Free Reward23 May
  12. 2024Token-level Direct Preference Optimization18 Apr
7 more
  1. 2024Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study16 Apr
  2. 2024ROPO: Robust Preference Optimization for Large Language Models5 Apr
  3. 2024Disentangling Length from Quality in Direct Preference Optimization28 Mar
  4. 2024Provably Robust DPO: Aligning Language Models with Noisy Feedback1 Mar
  5. 2024KTO: Model Alignment as Prospect Theoretic Optimization2 Feb
  6. 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation16 Jan
  7. 2023A General Theoretical Paradigm to Understand Learning from Human Preferences18 Oct

Other references · 13

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  3. 2023Instruction-Following Evaluation for Large Language Modelscited by 1,143
  4. 2024Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluatorscited by 865
  5. 2023UltraFeedback: Boosting Language Models with Scaled AI Feedbackcited by 394
  6. 2023Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraintcited by 346
  7. 2021Understanding Dataset Difficulty with V-Usable Informationcited by 288
  8. 2024Secrets of RLHF in Large Language Models Part II: Reward Modelingcited by 170
  9. 2024DPO Meets PPO: Reinforced Token Optimization for RLHFcited by 126
  10. 2025HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languagescited by 71
  11. 2024Rethinking Bradley-Terry Models in Preference-Based Reward Modeling: Foundations, Theory, and Alternativescited by 48
  12. 2024Towards Data-Centric RLHF: Simple Metrics for Preference Dataset Comparisoncited by 20
1 more
  1. 2022Efficiently Training Low-Curvature Neural Networkscited by 19

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  1. 2026Gradient-Gated DPO: Stabilizing Preference Optimization in Language Models
  2. 2026LSC-DPO: Learning-Signal-Controlled Direct Preference Optimization
  3. 2025Proximalized Preference Optimization for Diverse Feedback Types: A Decomposed Perspective on DPO
  4. 2025Preference Optimization by Estimating the Ratio of the Data Distribution
  5. 2024Direct Preference Optimization with an Offset
  6. 2026Mitigating Mismatch within Reference-based Preference Optimization
  7. 2024Understanding Reference Policies in Direct Preference Optimization
  8. 2025Differential Information Distribution: A Bayesian Perspective on Direct Preference Optimization