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Meet Dynamic Individual Preferences: Resolving Conflicting Human Value with Paired Fine-Tuning

Shanyong Wang, Shuhang Lin, Yining Zhao, Xi Zhu, Yongfeng Zhang

arXiv:2604.12479v1cs.CL

TL;DR

Individual preferences are diverse and dynamic, while broad alignment targets population-level preferences. The paper introduces VCD and PFT to address conflicting preferences with one model. PFT outperforms single-preference training across classification and open-ended generation, and supports customization from limited user history.

  • Problem

    Broad alignment optimizes population-level preferences, leaving models with limited ability to capture diverse and changing preferences at the individual level.

    Source: §1 · p. 1

  • Method

    The paper introduces VCD and PFT, a paradigm for aligning one model with both sides of contradictory preferences and generalizing across preference pairs.

    Source: §6 · p. 8

  • Results

    PFT outperforms single-preference training in classification and open-ended generation, reaches 96.67% accuracy on multi-choice classification (LLaMA-3.1-8B), and is more data-efficient than SFT and DPO.

    Source: §4.2 · p. 5, §6 · pp. 8–9

  • Takeaways & Limitations

    PFT supports rapid customization to individual users using only a few examples.

    Source: §6 · p. 9

    Experiments focus on a limited set of preference dimensions and do not capture the full complexity of real-world human values.

    Source: §7 · p. 9

Abstract

from arXiv · show

Recent advances in large language models (LLMs) have significantly improved the alignment of models with general human preferences. However, a major challenge remains in adapting LLMs to individual preferences, which are not only diverse but also dynamic. In this paper, we introduce a novel framework, Preference-Paired Fine-Tuning (PFT), designed to align models with contradictory and evolving individual preferences. We present a new dataset, Value Conflict Dilemma (VCD), which includes scenarios that involve conflicting human preferences, facilitating the evaluation of our approach. Our experiments demonstrate that PFT outperforms single-preference training methods, achieving up to 96.6% accuracy in multi-choice classification tasks and the highest open-ended generation score of 8.69. PFT also shows significant improvements over DPO, SFT and some traditional training methods, especially when handling conflicting preferences. Additionally, with limited user history data, models can inferring preference vector rapidly, achieving a 44.76% improvement in user-specific preference alignment in comparison to single-preference models.

1 Introduction

Population-level alignment leaves models ill-suited to diverse, changing individual preferences. Prior individual-alignment approaches use user histories or value-based alignment, but value-based methods face limits in adapting across preferences.

1 Introduction

  • Universal alignment optimizes broad, population-level preferences, leaving a gap in capturing diversity and variability at the individual level.A single user may have unique or idiosyncratic preferences that require case-by-case adaptation.Source: §1 · p. 1
  • Individual preferences vary across people and can shift within one person by task or mood, motivating user modeling and personalization.The paper gives the example of someone cautious in one situation but willing to take risks in another.Source: §1 · p. 1
  • Value-based methods can serve users with shared values, but non-adaptive methods may underperform and weight-adaptive methods typically handle one preference at a time, requiring separate models and added costs.The paper distinguishes historical-data approaches from value-based alignment; the cited limitations concern the latter.Source: §1 · p. 1

2. Real-world preference data is seldom avail-

The paper identifies a need to align from small, implicit user histories rather than relying on explicit preference statements. It introduces VCD and PFT, and proposes in-context learning for user-specific adaptation.

2. Real-world preference data is seldom avail-

  • Existing methods rely on explicit preference prompts at inference, while real-world preferences may instead appear as implicit behavioral traces and interaction histories.The paper frames alignment from small, implicit datasets as a key challenge.Source: §2 · p. 2
  • The Value Conflict Dilemma (VCD) dataset captures scenarios involving conflicting preferences to address a lack of high-quality evaluation resources.The paper also proposes PFT, a single-model paradigm for aligning with multiple, including contradictory, preferences.Source: §2 · p. 2
  • PFT is reported to improve classification and text generation even when trained only on single-choice data.The paper also proposes using in-context learning with limited user history to align with user preferences and generate higher-quality outputs.Source: §2 · p. 2
  • The paper presents VCD and PFT as a step toward one model adapting to diverse and contradictory individual preferences.It also proposes rapid user customization through a simple in-context learning approach.Source: §2 · p. 2

2 Related Work

The related work contrasts universal alignment and stable-preference assumptions with individual and context-dependent variation. It positions PFT as a way to retain multiple behavioral modes in one model.

2 Related Work

  • RLHF and later alignment methods have addressed quality through global notions such as helpfulness, honesty, and harmlessness (Christiano et al., 2017; Ouyang et al., 2022; Ziegler et al., 2019; Bai et al., 2022a; Zhou et al., 2023; Chen et al., 2024b; Khanov et al., 2024; Yang et al., 2024b).The passage also names DPO (Rafailov et al., 2023) and constitutional AI (Bai et al., 2022b) as approaches that streamline alignment.Source: §2 · p. 2
  • Universal-level methods optimize for consensus rather than individual variation, the gap this work aims to address.The passage names DPO (Rafailov et al., 2023) and constitutional AI (Bai et al., 2022b) among these approaches.Source: §2 · p. 2
  • Most existing supervised fine-tuning approaches assume stable, internally consistent preferences, whereas people can be context-dependent, such as creative in brainstorming but conservative in safety-critical situations (Lee et al., 2024; Cai et al., 2025; Dong et al., 2023a; Xiao et al., 2025).PFT is described as using scenario-conditioned contrastive pairs to maintain multiple behavioral modes with SFT's computational efficiency.Source: §2 · p. 2

3 Method

The method models preference-conditioned responses, represents user preferences as low-dimensional variation, and trains on opposing preference pairs. Its generalization analysis links paired training to improved preference coverage and reduced sensitivity.

3.1 Preliminaries

  • Given scenario x and preference descriptor p, the pretrained model defines a conditional distribution over responses; the goal is to generalize across unseen or mixed preferences from limited supervision.The model is parameterized by θ, and the preference descriptors come from a finite set.Source: §3.1 · p. 2

3.2 Preference Space and Manifold Structure

  • The method represents preference descriptors in a space whose induced metric captures correlations, and assumes each user's effective preferences lie on a smooth, low-dimensional manifold.The formulation treats low-dimensional variation as an assumption about preferences within the full preference space.Source: §3.2 · pp. 2–3

3.3 Preference Pairs

  • Conflicting preference pairs are proposed as informative directions for characterizing local variation, using opposing labels for the same scenario.Source: §3.3 · p. 3

3.4 Preference-Paired Fine-Tuning (PFT)

  • PFT synchronously optimizes both sides of each contradiction using a paired objective.The illustrated framework describes training a mixed model on paired data and then inferring a preference vector for adaptation.Source: §3.4 · p. 3, Figure 2 · p. 4

3.5 Preference-Manifold Generalization

  • PFT is analyzed as improving both the covering radius ε and preference sensitivity Lp relative to single-preference training, tightening worst-case risk control on the user-specific preference manifold.The analysis assumes bounded loss and a preference-Lipschitz loss; it attributes improved robustness and generalization to unseen or mixed preferences to reducing both quantities.Source: §3.5 · pp. 3–4
  • Paired anchors constrain the model along preference-difference directions, and a conflicting pair covers the segment between its endpoints, yielding a smaller effective covering radius than single-preference anchors.This is presented as requiring fewer anchors to cover local manifold regions.Source: §3.5 · p. 4

4 Experiment

The experiments evaluate PFT across preference-conditional tasks and datasets. PFT performs strongest overall, while customization and ablations examine adaptation and training choices.

4.1.1 Datasets and Evaluation Methods

  • VCD targets conflicting-value scenarios, while BQD provides broader behavioral reasoning; both datasets include multiple-choice and open-ended evaluation.VCD uniquely supports paired supervision, contextualized scenarios, and fine-grained evaluation.Source: §4.1.1 · p. 5, Table 1 · p. 5

4.1.2 Baselines

  • Baselines span prompted base models, supervised and direct-preference fine-tuning, controllable preference optimization, and training-free activation steering.The evaluated backbones are Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, and Llama-3.1-8B-Instruct.Source: §4.1.2 · p. 5

4.2 Results

  • PFT leads across VCD and BQD, reaching 96.67% multi-choice classification accuracy on LLaMA-3.1-8B and the highest open-ended score of 8.69.Paired-preference models also outperform single-preference and base models across most preference dimensions.Source: §4.2 · pp. 5, 7, Table 2 · p. 6, Figure 4 · p. 7

4.3 Preference Stimulation and Rapid Customization

  • PFT infers preference vectors from limited user history and uses them as prompts, with three history examples used for few-shot customization.The method is presented as faster to adapt than conventional training techniques.Source: §4.3 · p. 7

4.4 Ablation Study

  • PFT remains ahead of single-preference training with reduced data, with performance converging only at around 650 training examples.The main experiments use 1,000 examples as the default PFT training size.Source: §4.4 · p. 7
  • In multi-choice-All evaluation, the model performs best when λ+ = λ− = 1; setting either coefficient to zero performs poorly.The analysis uses VCD and Qwen2.5-3B-Instruct.Source: §4.4 · pp. 7–8, Figure 5 · p. 7

5 Discussion

Multi-pair training offers more balanced performance across preference types than single-pair training, which performs best on its target preference but generalizes less well.

5 Discussion

  • Multi-pair training gives more balanced results across four preference types, although per-pair accuracy may be slightly lower than single-pair training.The four types are risk, social, time, and decision-making.Source: §5 · p. 8, Figure 6 · p. 8
  • Multi (1k) delivers comparable average performance to Single Pair (1k) while covering multiple preference pairs; Multi (4k) further improves overall accuracy in the multi-preference setting.Source: §5 · p. 8
  • Single-pair training reaches the highest accuracy on its target preference, but does not generalize well to other preference types.The reported comparison covers four preference dimensions.Source: §5 · p. 8, Figure 6 · p. 8

6 Conclusion

The conclusion introduces VCD for evaluating conflicting preferences and PFT, which aligns one model with both sides of contradictory preferences and generalizes across preference pairs. The authors report gains over single-preference training in classification and open-ended generation, along with data efficiency and rapid user customization.

6 Conclusion

  • VCD is introduced as a dataset for evaluating scenarios involving conflicting human preferences.The authors describe it as a resource for assessing models' ability to navigate value-based conflicts.Source: §6 · p. 8
  • PFT aligns one model with both sides of contradictory preferences and generalizes across multiple preference pairs.The paper reports that it outperforms single-preference training in classification and open-ended generation.Source: §6 · p. 8
  • PFT is reported to be more data-efficient than SFT and DPO while supporting rapid customization from only a few user examples.The conclusion frames PFT as a scalable and practical approach to personalized, conflict-aware LLMs.Source: §6 · p. 9

7 Limitations

The paper identifies limits in the preference dimensions studied, its use of automated raters, and its support for adaptation over time. It frames PFT as an initial step toward conflict-aware personalization and describes four value-conflict dimensions used in VCD.

7 Limitations

  • The experiments cover a limited set of preference dimensions and do not capture the full complexity of real-world human values.The authors call for broader preferences, including less binary and more context-dependent ones.Source: §7 · p. 9
  • Automated GPT-based raters can exhibit systematic biases, including a preference for fluency, despite high agreement with human judgments.The authors propose larger-scale human evaluation and cross-cultural annotator groups as future work.Source: §7 · p. 9
  • PFT does not yet address long-term preference tracking or continuous adaptation in dynamic settings.The authors suggest exploring online or reinforcement-learning extensions that update alignment signals over time without compromising stability.Source: §7 · p. 9
  • The authors characterize PFT as a first step toward conflict-aware personalization and identify applicability, robustness, and fairness as areas for future extension.They frame this as relevant to real-world deployment.Source: §7 · p. 9

A.1.1 Preference Definition

  • VCD defines conflicts across risk, social, time, and decision-making preferences, including risk-taking versus risk-averse and intuitive versus analytical approaches.The paper presents these as representative dimensions of conflicting human values.Source: §A.1.1 · p. 12

A.1.2 Dataset Generation

The appendix describes VCD scenario generation and evaluation procedures, alongside selection and definition details for BQD behaviors. It also reports human checks of VCD label consistency and whether choices map exclusively to one side of a preference conflict.

A.1.2 Dataset Generation

  • For VCD, GPT-4o generates scenarios and corresponding choices using a template; scenarios have two to five multiple-choice options.The dataset-generation template and option-count details are shown in Figures 8 and 9.Source: §A.1.2 · p. 13, Figure 8 · p. 13, Figure 9 · p. 13

A.1.3 Open-ended question Evaluation Prompt

  • The appendix points to Figure 13 for GPT-4o-mini rating prompts used to evaluate open-ended responses.These prompts concern evaluation of open-ended generation across methods.Source: §A.1.3 · p. 13

A.2.1 Dataset Selection

  • BQD includes seven behaviors, of which Hallucination, Myopic Reward, and Sycophancy are selected.The passage says some behaviors leak enough training data and others are not directly related to individual preferences.Source: §A.2.1 · p. 13

A.2.2 Preference Definition

B Dataset Evaluation

  • The One protocol requires exactly one choice aligned with the target preference, while the All protocol evaluates a selected subset against the gold set.For One, the gold set contains choices annotated with the target preference.Source: B Dataset Evaluation · p. 14

C Human Evaluation

  • Human evaluation recruited qualified annotators from the United States and China, including internal annotators with computer science or linguistics PhDs or doctoral study.The appendix reports 30 qualified annotators and four internal high-quality annotators.Source: C Human Evaluation · p. 14

C.1 Evaluation on Synthetic Datasets

  • The synthetic-dataset evaluation asked whether preference labels matched human judgments and whether each option aligned with only one side of a preference pair.The reported results support label consistency and near-exclusive mapping of options to one side.Source: §C.1 · p. 14
  • 98.29% of automatically generated VCD labels were consistent with human judgment, with 97.23% agreement across annotators.Annotators also confirmed that nearly all options mapped exclusively to one side of a preference conflict.Source: §C.1 · p. 14

C.2 Evaluation on GenAI of Open-ended Question

Human review found that 83% of GPT-assigned open-ended scores reasonably reflected preference alignment, while also identifying biases that make human validation important. The appendix describes sequential and synchronous paired updates and a risk-bound analysis connecting preference coverage to PFT.

C.2 Evaluation on GenAI of Open-ended Question

  • 83% of GPT-assigned scores were judged reasonable by human annotators in the open-ended evaluation.Each output received an average of two independent human judgments.Source: §C.2 · p. 14
  • GPT raters sometimes over-penalized neutral or ambiguous reasoning and gave higher scores for fluent answers despite imperfect preference alignment.The authors conclude that human validation remains necessary to detect systematic biases.Source: §C.2 · pp. 14–15

D.1 Asynchronous Updates

  • Asynchronous updates apply gradients for opposite preferences sequentially, creating second-order coupling and potentially order-dependent convergence.The loss gradients are weighted by λ+ and λ−.Source: §D.1 · p. 15

D.2 Synchronous Updates

  • Synchronous updates combine both preferences in each parameter update to eliminate order dependence and second-order interference.The implementation uses token-level cross-entropy loss.Source: §D.2 · p. 15

E.1 Model Version

  • Experiments use Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct, and Llama-3.1-8B-Instruct, with approximately 1K training examples per dataset.Inference uses temperature 0.1, top-k 0.9, and a maximum generation length of 512 tokens; CAA uses layer 16.Source: §E.1 · p. 15, §E.2 · p. 15, §E.6 · p. 16

F.1 Setup

  • The theoretical setup treats user preferences as a manifold and uses an ε-net to approximate it, with a Lipschitz assumption bounding loss variation across preferences.The net's minimum size is represented by the covering number N(Mu, ε).Source: §F.1 · p. 16, §F.2 · p. 16, §F.3 · p. 16

F.4 Concentration on the ε-Net (Hoeffding + Union Bound)

  • Hoeffding’s inequality and a union bound provide a high-probability uniform concentration result over the ε-net anchors.The analysis combines this concentration result with discretization and notes a stability assumption on D(p).Source: §F.4 · p. 17, §F.5 · p. 17

F.6 Remarks: Connecting to PFT

  • The analysis suggests reducing the covering radius or preference sensitivity to tighten the bound, and says paired training empirically decreases effective preference sensitivity.PFT trains on conflicting preference anchors for the same scenario, supporting robustness on unseen or mixed preferences along the manifold.Source: §F.6 · p. 17

G More Experiments

Across tests of reasoning-data sources, the reported differences were marginal, while MMLU performance did not significantly change with the intervention. The paper also identifies risks from preference adaptation and synthetic or automated evaluation, and describes its use of AI writing and coding assistance.

G More Experiments

  • Differences across ChatGPT-, Claude-, and DeepSeek-generated CoT data stayed within 0.02–0.03 in accuracy and under 0.2 in human scores.The authors attribute the gains to PFT rather than distillation from a specific generator.Source: G More Experiments · p. 17, Table 6 · p. 18
  • The authors report that their intervention did not significantly affect MMLU performance.Table 7 contains the detailed MMLU results.Source: G More Experiments · p. 18, Table 7 · p. 19

H The Use of Large Language Models (LLMs)

  • ChatGPT assisted with writing and CodePilot with coding, and the authors state that AI-generated components were manually checked and modified.They state that the paper contains no fully AI-generated content.Source: H The Use of Large Language Models… · p. 18

I Ethics Statement

  • The authors flag risks of reinforcing harmful preferences, misuse for manipulation, and cultural or social bias in synthetic data and GPT-based evaluation.They report using human validation and label their intended work as conflict-aware alignment research, not persuasive or deceptive applications.Source: I Ethics Statement · p. 18

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