Losing set is identical to the reference output

Oct 13, 2025 by magicalceylontours.com 37 views

[Not Paper] Papers for Mitigating Imbalance in Preference Optimization (in View of the Pairwise and Error Ratio)

Introduction

Preference optimization is a crucial task in various applications, including natural language processing, computer vision, and recommender systems. However, one of the significant challenges in preference optimization is the imbalance between the winning and losing sets. The winning set consists of instances where the model's output matches the ground truth, while the losing set comprises instances where the model's output does not match the ground truth. This imbalance can lead to overfitting and poor generalization performance.

In this article, we will discuss the issue of imbalance in preference optimization and explore some of the recent papers that address this problem. We will also introduce a new approach called Anchored Preference Optimization and Contrastive Revisions (APO), which aims to mitigate the imbalance between the winning and losing sets.

Problem Analysis

During the execution of the DPO (Deep Preference Optimization) algorithm, we encountered an issue where the learning process was easily terminated. To investigate this issue, we analyzed the following two possible causes:

Losing set is identical to the reference output: If the losing set is identical to the reference output, it may not be necessary to focus on the winning set. However, our analysis suggests that this is not the case.
Winning set has a very low error rate: The winning set has a very low error rate, which may lead to overfitting and poor generalization performance. We attempted to alleviate this issue by relaxing the similarity threshold between the ground truth and the model's output. However, this had a minimal effect.

Related Work

To address the imbalance issue, we need to develop a preference optimization method that takes into account the importance of both the winning and losing sets. One of the recent papers that addresses this problem is Anchored Preference Optimization and Contrastive Revisions (APO).

APO (Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment)

APO is a novel approach that aims to mitigate the imbalance between the winning and losing sets. The key insights of APO are as follows:

Create sets with modified instances: When creating sets, APO suggests modifying some instances to create a new set. This approach is similar to online DPO and can help alleviate the imbalance issue.
Select loss based on model confidence: APO allows selecting the loss based on the confidence of the reference model and the winning model. If the winning model is significantly better than the reference model, APO uses APO_zero (similar to KTO). Otherwise, APO uses APO_down.

Loss and Gradients

APO introduces a new loss function that takes into account the confidence of the reference model and the winning model. The loss function is as follows:

APO_zero: If the winning model is significantly better than the reference model, APO uses APO_zero, which is similar to KTO.
APO_down: If the winning model is not significantly better than the reference model, APO uses APO_down.

The gradients of the loss function are computed as follows:

Gradients of APO_zero: The gradients of APO_zero are computed using the KTO loss function.
Gradients of APO_down: The gradients of APO_down are computed using a modified version of the KTO loss function.

Conclusion

In this article, we discussed the issue of imbalance in preference optimization and explored some of the recent papers that address this problem. We introduced a new approach called Anchored Preference Optimization and Contrastive Revisions (APO), which aims to mitigate the imbalance between the winning and losing sets. APO creates sets with modified instances and selects the loss based on the confidence of the reference model and the winning model. The loss function and gradients of APO are computed using a modified version of the KTO loss function.

Future Work

Future work includes:

Experimental evaluation: We plan to conduct experimental evaluation of APO on various preference optimization tasks to demonstrate its effectiveness.
Comparison with other methods: We plan to compare APO with other preference optimization methods to demonstrate its superiority.
Extension to other tasks: We plan to extend APO to other tasks, such as natural language processing and computer vision.

References

[1] Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment. Paper
[2] Deep Preference Optimization. Paper
[3] KTO: A Novel Loss Function for Preference Optimization. Paper
[Not Paper] Papers for Mitigating Imbalance in Preference Optimization (in View of the Pairwise and Error Ratio)

Q&A

In this section, we will address some of the frequently asked questions related to preference optimization and the imbalance issue.

Q: What is the main cause of the imbalance issue in preference optimization?

A: The main cause of the imbalance issue in preference optimization is the difference in the number of instances between the winning and losing sets. The winning set typically has a much larger number of instances than the losing set, which can lead to overfitting and poor generalization performance.

Q: How can we alleviate the imbalance issue in preference optimization?

A: There are several ways to alleviate the imbalance issue in preference optimization. One approach is to use data augmentation techniques to increase the number of instances in the losing set. Another approach is to use class weighting or oversampling to balance the number of instances between the winning and losing sets.

Q: What is the difference between APO and other preference optimization methods?

A: APO is a novel approach that aims to mitigate the imbalance between the winning and losing sets. Unlike other preference optimization methods, APO creates sets with modified instances and selects the loss based on the confidence of the reference model and the winning model.

Q: How does APO select the loss function?

A: APO selects the loss function based on the confidence of the reference model and the winning model. If the winning model is significantly better than the reference model, APO uses APO_zero (similar to KTO). Otherwise, APO uses APO_down.

Q: What are the advantages of APO over other preference optimization methods?

A: APO has several advantages over other preference optimization methods. Firstly, APO can alleviate the imbalance issue between the winning and losing sets. Secondly, APO can select the loss function based on the confidence of the reference model and the winning model. Finally, APO can improve the generalization performance of the model.

Q: What are the limitations of APO?

A: APO has several limitations. Firstly, APO requires a large amount of data to train the model. Secondly, APO can be computationally expensive to train. Finally, APO may not work well with imbalanced datasets.

Q: How can we extend APO to other tasks?

A: APO can be extended to other tasks by modifying the loss function and the gradients. For example, APO can be extended to natural language processing by using a language model as the reference model. APO can also be extended to computer vision by using a convolutional neural network as the reference model.