Leveraging Robust Optimization for LLM Alignment under Distribution Shifts

• URL: http://arxiv.org/abs/2504.05831v1
• Date: Tue, 08 Apr 2025 09:14:38 GMT
• Title: Leveraging Robust Optimization for LLM Alignment under Distribution Shifts
• Authors: Mingye Zhu, Yi Liu, Junbo Guo, Quan Wang, Yongdong Zhang, Zhendong Mao,
• Abstract summary: Large language models (LLMs) increasingly rely on preference alignment methods to steer outputs toward human values.<n>Recent approaches have turned to synthetic data generated by LLMs as a scalable alternative.<n>We propose a novel distribution-aware optimization framework that improves preference alignment in the presence of such shifts.
• Score: 54.654823811482665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) increasingly rely on preference alignment methods to steer outputs toward human values, yet these methods are often constrained by the scarcity of high-quality human-annotated data. To tackle this, recent approaches have turned to synthetic data generated by LLMs as a scalable alternative. However, synthetic data can introduce distribution shifts, compromising the nuanced human preferences that are essential for desirable outputs. In this paper, we propose a novel distribution-aware optimization framework that improves preference alignment in the presence of such shifts. Our approach first estimates the likelihood ratios between the target and training distributions leveraging a learned classifier, then it minimizes the worst-case loss over data regions that reflect the target human-preferred distribution. By explicitly prioritizing the target distribution during optimization, our method mitigates the adverse effects of distributional variation and enhances the generation of responses that faithfully reflect human values.

Related papers

• Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model [84.00480999255628]
  Reinforcement Learning algorithms for safety alignment of Large Language Models (LLMs) encounter the challenge of distribution shift.<n>Current approaches typically address this issue through online sampling from the target policy.<n>We propose a new framework that leverages the model's intrinsic safety judgment capability to extract reward signals.
  arXiv  Detail & Related papers  (2025-03-13T06:40:34Z)
• Direct Distributional Optimization for Provable Alignment of Diffusion Models [39.048284342436666]
  We introduce a novel alignment method for diffusion models from distribution optimization perspectives. We first formulate the problem as a generic regularized loss minimization over probability distributions. We enable sampling from the learned distribution by approximating its score function via Doob's $h$-transform technique.
  arXiv  Detail & Related papers  (2025-02-05T07:35:15Z)
• Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness [27.43137305486112]
  We propose a novel Self-supervised Preference Optimization (SPO) framework, which constructs a self-supervised preference degree loss combined with the alignment loss. The results demonstrate that SPO can be seamlessly integrated with existing preference optimization methods to achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-09-26T12:37:26Z)
• Geometric-Averaged Preference Optimization for Soft Preference Labels [78.2746007085333]
  Many algorithms for aligning LLMs with human preferences assume that human preferences are binary and deterministic. In this work, we introduce the distributional soft preference labels and improve Direct Preference Optimization (DPO) with a weighted geometric average of the LLM output likelihood in the loss function.
  arXiv  Detail & Related papers  (2024-09-10T17:54:28Z)
• Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment [72.99676237703099]
  We propose a new framework that boosts the alignment of large language models with human preferences. Our key idea is leveraging the human prior knowledge within the small (seed) data. We introduce a noise-aware preference learning algorithm to mitigate the risk of low quality within generated preference data.
  arXiv  Detail & Related papers  (2024-06-06T18:01:02Z)
• Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer [52.09480867526656]
  We identify the source of misalignment as a form of distributional shift and uncertainty in learning human preferences.<n>To mitigate overoptimization, we first propose a theoretical algorithm that chooses the best policy for an adversarially chosen reward model.<n>Using the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines a preference optimization loss and a supervised learning loss.
  arXiv  Detail & Related papers  (2024-05-26T05:38:50Z)
• Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization [37.8788435790632]
  Large language models (LLMs) have revolutionized the role of AI, yet pose potential social risks. Existing methods rely on high-quality positive-negative training pairs, suffering from noisy positive responses that are barely distinguishable from negative ones. We propose Distributional Dispreference Optimization (D$2$O), which maximizes the discrepancy between dispreferred responses and the generated non-negative ones.
  arXiv  Detail & Related papers  (2024-03-06T03:02:38Z)
• Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game [31.66896160733569]
  We propose an Adversarial Preference Optimization (APO) framework to target more efficient human preference optimization. We find the proposed adversarial training framework further enhances existing alignment baselines in terms of LLM helpfulness and harmlessness.
  arXiv  Detail & Related papers  (2023-11-14T10:10:31Z)
• FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations [53.268801169075836]
  We propose FedLAP-DP, a novel privacy-preserving approach for federated learning. A formal privacy analysis demonstrates that FedLAP-DP incurs the same privacy costs as typical gradient-sharing schemes. Our approach presents a faster convergence speed compared to typical gradient-sharing methods.
  arXiv  Detail & Related papers  (2023-02-02T12:56:46Z)
• KL Guided Domain Adaptation [88.19298405363452]
  Domain adaptation is an important problem and often needed for real-world applications. A common approach in the domain adaptation literature is to learn a representation of the input that has the same distributions over the source and the target domain. We show that with a probabilistic representation network, the KL term can be estimated efficiently via minibatch samples.
  arXiv  Detail & Related papers  (2021-06-14T22:24:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.