Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective

• URL: http://arxiv.org/abs/2404.04626v1
• Date: Sat, 6 Apr 2024 13:24:37 GMT
• Title: Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective
• Authors: Duanyu Feng, Bowen Qin, Chen Huang, Zheng Zhang, Wenqiang Lei,
• Abstract summary: We provide an analytical framework using the field theory to analyze the optimization process of DPO. We find that the DPO loss function decreases the probability of producing human dispreferred data at a faster rate than it increases the probability of producing preferred data.
• Score: 25.34250859820326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Direct Preference Optimization (DPO), which derives reward signals directly from pairwise preference data, has shown its effectiveness on aligning Large Language Models (LLMs) with human preferences. Despite its widespread use across various tasks, DPO has been criticized for its sensitivity to the SFT's effectiveness and its hindrance to the learning capacity towards human-preferred responses, leading to less satisfactory performance. To overcome those limitations, the theoretical understanding of DPO are indispensable but still lacking. To this end, we take a step towards theoretically analyzing and understanding the limitations of DPO. Specifically, we provide an analytical framework using the field theory to analyze the optimization process of DPO. By analyzing the gradient vector field of the DPO loss function, we find that the DPO loss function decreases the probability of producing human dispreferred data at a faster rate than it increases the probability of producing preferred data. This provides theoretical insights for understanding the limitations of DPO discovered in the related research experiments, thereby setting the foundation for its improvement.

Related papers

• Uncertainty-Penalized Direct Preference Optimization [52.387088396044206]
  We develop a pessimistic framework for DPO by introducing preference uncertainty penalization schemes. The penalization serves as a correction to the loss which attenuates the loss gradient for uncertain samples. We show improved overall performance compared to vanilla DPO, as well as better completions on prompts from high-uncertainty chosen/rejected responses.
  arXiv  Detail & Related papers  (2024-10-26T14:24:37Z)
• A Comprehensive Survey of Datasets, Theories, Variants, and Applications in Direct Preference Optimization [52.42860559005861]
  Direct Preference Optimization (DPO) has emerged as a promising approach for alignment. Despite DPO's various advancements and inherent limitations, an in-depth review of these aspects is currently lacking in the literature.
  arXiv  Detail & Related papers  (2024-10-21T02:27:24Z)
• TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights [73.9088920210495]
  We propose a token-level importance sampling DPO objective named TIS-DPO that assigns importance weights to each token based on its reward. TIS-DPO significantly outperforms various baseline methods on harmlessness and helpfulness alignment and summarization tasks.
  arXiv  Detail & Related papers  (2024-10-06T04:03:00Z)
• Length Desensitization in Directed Preference Optimization [26.664176443756773]
  It has been observed that DPO tends to over-optimize for verbosity, which can detrimentally affect both performance and user experience. We propose a length-desensitization improvement method for DPO, termed LD-DPO. The proposed method aims to desensitize DPO to data length by decoupling explicit length preference, which is relatively insignificant, from the other implicit preferences.
  arXiv  Detail & Related papers  (2024-09-10T10:49:38Z)
• The Hitchhiker's Guide to Human Alignment with *PO [43.4130314879284]
  We focus on identifying the algorithm that, while being performant, is simultaneously more robust to varying hyper parameters. Our analysis reveals that the widely adopted DPO method consistently produces lengthy responses of inferior quality. Motivated by these findings, we propose an embarrassingly simple extension to the DPO algorithm, LN-DPO, resulting in more concise responses without sacrificing quality.
  arXiv  Detail & Related papers  (2024-07-21T17:35:20Z)
• Understanding Reference Policies in Direct Preference Optimization [50.67309013764383]
  Direct Preference Optimization (DPO) has become a widely used training method for the instruction fine-tuning of large language models (LLMs) This work explores an under-investigated aspect of DPO - its dependency on the reference model or policy.
  arXiv  Detail & Related papers  (2024-07-18T17:08:10Z)
• 3D-Properties: Identifying Challenges in DPO and Charting a Path Forward [17.27880657597116]
  We revisit DPO with a comprehensive examination of its empirical efficacy and a systematic comparison with RLHF-PPO. We identify the textbf3D-properties of DPO's learning outcomes. We propose easy regularization methods to mitigate the issues caused by textbf3D-properties.
  arXiv  Detail & Related papers  (2024-06-11T14:59:24Z)
• Secrets of RLHF in Large Language Models Part I: PPO [81.01936993929127]
  Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. reinforcement learning with human feedback (RLHF) emerges as the pivotal technological paradigm underpinning this pursuit. In this report, we dissect the framework of RLHF, re-evaluate the inner workings of PPO, and explore how the parts comprising PPO algorithms impact policy agent training.
  arXiv  Detail & Related papers  (2023-07-11T01:55:24Z)
• Fine-Tuning Language Models with Advantage-Induced Policy Alignment [80.96507425217472]
  We propose a novel algorithm for aligning large language models to human preferences. We show that it consistently outperforms PPO in language tasks by a large margin. We also provide a theoretical justification supporting the design of our loss function.
  arXiv  Detail & Related papers  (2023-06-04T01:59:40Z)

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.