Text as a Universal Interface for Transferable Personalization
AlignXplore+ uses text as a universal interface for cross-task personalization, outperforming larger models on nine benchmarks with an 8B model.
Key Findings
Methodology
The paper introduces a two-stage training framework combining supervised fine-tuning with reinforcement learning to generate interpretable user preference text summaries. Initially, high-quality training data is generated through a 'generate-validate-merge' pipeline for fine-tuning, followed by reinforcement learning using curriculum pruning and cumulative reward optimization strategies.
Key Results
- AlignXplore+ achieves an average score of 75.10% across nine benchmarks, 4.2% higher than GPT-OSS-20B, with significant gains in complex reasoning tasks.
- In cross-task transfer, text summaries provide up to 14.00% absolute improvement across different tasks and show consistent performance across model architectures.
- When handling complex user histories, the model improves by 2.36% over Qwen3-8B on positive-only logs.
Significance
This research addresses the challenges of interpretability and cross-task transferability in traditional user preference representations by transforming them into interpretable text. It advances academic research in personalization and offers new ways for industry to manage and apply user data.
Technical Contribution
The technical contributions include a novel user preference representation paradigm and an innovative training framework combining supervised fine-tuning and reinforcement learning, offering enhanced cross-task transferability and interpretability compared to existing methods.
Novelty
This study is the first to use natural language as a universal interface for user preferences, breaking the limitations of traditional vector and parameterized methods by providing interpretable and transferable preference representations.
Limitations
- The model may perform poorly when handling extremely complex or noisy user histories, requiring further optimization.
- High computational resource demands may limit its application in resource-constrained environments.
Future Work
Future research could explore extensions in more domains and applications, such as real-time personalization and integration of multimodal data.
AI Executive Summary
Personalization is a cornerstone of modern AI applications, especially as large language models (LLMs) transition from general-purpose tools to user-centric products. Traditionally, user preferences are represented as implicit, model-specific vectors or parameters, which are difficult to interpret and transfer across models and tasks. This paper proposes using natural language as a universal interface for user preference representation, enabling cross-task and cross-model preference transfer through text summaries of user history.
AlignXplore+ is a universal preference reasoning model developed based on this framework, employing a two-stage training framework: first, generating high-quality training data through a 'generate-validate-merge' process for supervised fine-tuning, followed by reinforcement learning using curriculum pruning and cumulative reward optimization strategies. Experimental results show that AlignXplore+ achieves state-of-the-art performance across nine benchmarks, particularly excelling in complex reasoning tasks.
This research not only advances academic research in personalization but also offers new ways for industry to manage and apply user data. However, the model may perform poorly when handling extremely complex or noisy user histories. Future research could explore extensions in more domains and applications, such as real-time personalization and integration of multimodal data.
Deep Analysis
Background
Personalization technology plays a crucial role in the development of AI, especially in applications like recommendation systems and personalized response generation. Traditional methods often use vectors or parameterized representations of user preferences, but these methods face challenges in interpretability and cross-task transferability.
Core Problem
Traditional user preference representations are difficult to transfer across tasks and models and lack interpretability. This limits users' understanding and control over their digital profiles and leads to data silos.
Innovation
This paper innovatively proposes using natural language as a universal interface for user preferences, enabling cross-task and cross-model preference transfer through text summaries. Compared to traditional methods, this approach offers greater interpretability and transferability.
Methodology
- �� Supervised fine-tuning: Generate high-quality training data through a 'generate-validate-merge' process.
- �� Reinforcement learning: Use curriculum pruning and cumulative reward optimization strategies to enhance long-term utility and cross-task transferability.
Experiments
Experiments were conducted across nine benchmarks, including recommendation, response selection, and response generation tasks. Models used include Qwen3-8B and other open-source models, with accuracy as the evaluation metric.
Results
AlignXplore+ achieves an average score of 75.10% across nine benchmarks, 4.2% higher than GPT-OSS-20B. In cross-task transfer, text summaries provide up to 14.00% absolute improvement across different tasks.
Applications
This method can be applied in scenarios such as personalized recommendation and response generation, particularly suitable for applications requiring high interpretability and cross-task transferability.
Limitations & Outlook
The model may perform poorly when handling extremely complex or noisy user histories, and its high computational resource demands may limit its application in resource-constrained environments.
Plain Language Accessible to non-experts
Imagine you're in a library, and traditional methods are like turning your reading records into a string of codes that only specific machines can understand. This paper's method, however, writes your reading preferences into an article that anyone can understand. It's like translating complex math formulas into simple text descriptions so everyone can understand your preferences.
ELI14 Explained like you're 14
Imagine you're playing a game, and your character has lots of skills and gear. Traditional methods are like hiding this information in a sealed box that only a specific key can open. This paper's method is like writing this information in a book that anyone can flip through. This allows your character to be effective in different games without having to relearn everything.
Glossary
Large Language Model (LLM)
A large-scale neural network model capable of understanding and generating natural language.
Used to generate text summaries of user preferences.
Supervised Fine-tuning
Fine-tuning a model using labeled data to improve its performance on specific tasks.
Used to generate high-quality training data.
Reinforcement Learning
A machine learning method that optimizes strategies through reward signals.
Used to optimize the model's long-term utility and cross-task transferability.
Curriculum Pruning
A strategy for selecting challenging yet tractable training samples.
Used to improve the model's learning efficiency.
Cumulative Reward
A reward function that considers long-term utility.
Used to optimize the model's long-term utility.
Open Questions Unanswered questions from this research
- 1 How can this method be efficiently applied in resource-constrained environments?
- 2 How to further improve the model's performance on extremely complex user histories?
Applications
Immediate Applications
Personalized Recommendation
Achieve more accurate personalized recommendations through text summaries of user preferences. Applicable in e-commerce, news, etc.
Long-term Vision
Cross-Platform User Data Management
Achieve cross-platform user data management and application through a unified text interface.
Abstract
We study the problem of personalization in large language models (LLMs). Prior work predominantly represents user preferences as implicit, model-specific vectors or parameters, yielding opaque ``black-box'' profiles that are difficult to interpret and transfer across models and tasks. In contrast, we advocate natural language as a universal, model- and task-agnostic interface for preference representation. The formulation leads to interpretable and reusable preference descriptions, while naturally supporting continual evolution as new interactions are observed. To learn such representations, we introduce a two-stage training framework that combines supervised fine-tuning on high-quality synthesized data with reinforcement learning to optimize long-term utility and cross-task transferability. Based on this framework, we develop AlignXplore+, a universal preference reasoning model that generates textual preference summaries. Experiments on nine benchmarks show that our 8B model achieves state-of-the-art performanc -- outperforming substantially larger open-source models -- while exhibiting strong transferability across tasks, model families, and interaction formats.