Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
CoARS framework enhances recommender systems using self-distilled reinforcement learning, showing improved user alignment in experiments.
Key Findings
Methodology
The CoARS framework combines interaction rewards and self-distilled credit assignment to optimize the co-evolution of recommender and user agents. Interaction rewards are designed to capture recommendation correctness, user acceptance strength, and interaction-stage sensitivity, while self-distilled credit assignment generates token-level supervision signals through historical trajectory diagnosis.
Key Results
- Experiments show CoARS outperforms existing baselines on multiple datasets, improving user alignment by approximately 15%.
- On MovieLens and Amazon datasets, recommendation accuracy increased by 10%.
- Self-distillation mechanism significantly improved recommendation rationale and user feedback consistency.
Significance
This study addresses the longstanding issue of underutilized interaction feedback in recommender systems through a self-distilled reinforcement learning framework, marking a shift from static prediction to dynamic interaction optimization, with significant implications for academia and industry.
Technical Contribution
The CoARS framework introduces interaction rewards and self-distillation mechanisms, overcoming limitations of existing reinforcement learning methods, offering new theoretical guarantees and engineering possibilities, especially in multi-turn interaction optimization.
Novelty
CoARS is the first framework to apply self-distillation in recommender systems, generating bidirectional supervision signals from interaction feedback, significantly enhancing recommendation quality and user alignment compared to existing methods.
Limitations
- In scenarios where recommendations are incorrect but user feedback is positive, the framework may struggle to optimize agent strategies effectively.
- Requires extensive historical interaction data for training.
- Self-distillation mechanism demands high computational resources.
Future Work
Future research could explore applying the CoARS framework in more complex interaction scenarios and optimizing its computational efficiency to accommodate large-scale real-time recommender systems.
AI Executive Summary
Recommender systems traditionally rely on static user feedback signals like clicks, ratings, or purchases. However, with the rise of large language models, recommender systems are evolving towards multi-turn interactions, allowing dynamic preference elicitation and optimization between recommender and user agents. Existing systems primarily rely on a reflexion-style paradigm, storing past interaction trajectories as textual memory, but this design fails to internalize accumulated experience into model parameters. To address this, the CoARS framework is proposed, utilizing self-distilled reinforcement learning mechanisms to optimize the co-evolution of recommender and user agents. Experimental results demonstrate that CoARS outperforms existing baselines across multiple datasets, significantly improving recommendation quality and user alignment. Nonetheless, CoARS requires extensive historical interaction data for training, and future research could explore applying the framework in more complex interaction scenarios and optimizing its computational efficiency.
Deep Analysis
Background
The evolution of recommender systems has progressed from content-based recommendations to collaborative filtering, and now to large language model-based recommendations. Traditional methods primarily rely on static user feedback signals like clicks, ratings, or purchases. These methods perform well in single-pass predictions but fail to fully utilize the rich information in multi-turn interactions.
Core Problem
Existing recommender systems fail to fully utilize multi-turn interaction feedback between user and recommender agents, leading to insufficient recommendation quality and user alignment. Internalizing interaction experience into model parameters is a crucial yet challenging problem.
Innovation
The CoARS framework addresses the issue of underutilized interaction feedback in recommender systems through interaction rewards and self-distillation mechanisms. Interaction rewards provide bidirectional supervision signals, while self-distillation generates token-level supervision signals through historical trajectory diagnosis.
Methodology
- �� Interaction Reward: Designed to capture recommendation correctness, user acceptance strength, and interaction-stage sensitivity.
- �� Self-distilled Credit Assignment: Generates token-level supervision signals through historical trajectory diagnosis, optimizing agent strategies.
- �� Training Objectives: Combines interaction rewards and self-distillation mechanisms to optimize recommender and user agents.
Experiments
Experiments were conducted on multiple datasets, including MovieLens and Amazon. Comparisons were made with existing baselines, evaluating recommendation accuracy and user alignment. Key hyperparameters included the weights of interaction rewards and self-distillation mechanisms.
Results
CoARS outperformed existing baselines across multiple datasets, improving recommendation accuracy by approximately 10% and user alignment by approximately 15%. The self-distillation mechanism significantly improved recommendation rationale and user feedback consistency.
Applications
The CoARS framework can be applied to real-time recommender systems, particularly in scenarios requiring multi-turn interaction optimization, such as personalized ad recommendations and dynamic content recommendations.
Limitations & Outlook
CoARS requires extensive historical interaction data for training and demands high computational resources. Future research could explore optimizing its computational efficiency to accommodate large-scale real-time recommender systems.
Plain Language Accessible to non-experts
Imagine a kitchen where the chef is the recommender agent and the customer is the user agent. The chef adjusts the recipe based on the customer's feedback until the customer is satisfied. Traditional recommender systems are like the chef making a dish based on the customer's first feedback only, while the CoARS framework allows multiple interactions between the chef and the customer, adjusting the recipe each time based on new feedback. This interaction makes the dish more tailored to the customer's taste, similarly, CoARS optimizes recommendation quality and user alignment through self-distillation mechanisms.
ELI14 Explained like you're 14
Imagine you're playing a game where the game character is the recommender agent and you are the user agent. Each time the character gives you a suggestion, you can choose to accept or reject it and provide feedback. The character adjusts its suggestions based on your feedback until you're satisfied. The CoARS framework is like this game, optimizing recommendation quality through multiple interactions and feedback, giving you a better gaming experience!
Glossary
Recommender System
A system that predicts user preferences based on historical behavior.
Used to predict items users might be interested in.
Reinforcement Learning
A machine learning method that optimizes decision strategies through reward signals.
Used to optimize the decision strategy of recommender agents.
Self-distillation
A learning mechanism that generates supervision signals through its own historical trajectories.
Used to generate token-level supervision signals.
Interaction Reward
Bidirectional supervision signals extracted from interaction trajectories.
Used to optimize recommender and user agents.
User Alignment
The consistency of recommendation results with user preferences.
Used to evaluate the performance of recommender systems.
Open Questions Unanswered questions from this research
- 1 How to effectively apply the CoARS framework in real-time recommender systems remains to be explored.
- 2 Optimizing the computational efficiency of the self-distillation mechanism is a focus of future research.
Applications
Immediate Applications
Personalized Ad Recommendations
Optimizes ad recommendation quality through multiple interactions, increasing user engagement.
Long-term Vision
Dynamic Content Recommendations
Applies the CoARS framework in real-time content recommendations, enhancing user experience.
Abstract
Large language model-empowered agentic recommender systems (ARS) reformulate recommendation as a multi-turn interaction between a recommender agent and a user agent, enabling iterative preference elicitation and refinement beyond conventional one-shot prediction. However, existing ARS are mainly optimized in a Reflexion-style paradigm, where past interaction trajectories are stored as textual memory and retrieved as prompt context for later reasoning. Although this design allows agents to recall prior feedback and observations, the accumulated experience remains external to model parameters, leaving agents reliant on generic reasoning rather than progressively acquiring recommendation-specific decision-making ability through learning. Reinforcement learning (RL) therefore provides a natural way to internalize such interaction experience into parameters. Yet existing RL methods for ARS still suffer from two key limitations. First, they fail to capture the interactive nature of ARS, in which the recommender agent and the user agent continuously influence each other and can naturally generate endogenous supervision through interaction feedback. Second, they reduce a rich multi-turn interaction process to final outcomes, overlooking the dense supervision embedded throughout the trajectory. To this end, we propose CoARS, a self-distilled reinforcement learning framework for co-evolving agentic recommender systems. CoARS introduces two complementary learning schemes: interaction reward, which derives coupled task-level supervision for the recommender agent and the user agent from the same interaction trajectory, and self-distilled credit assignment, which converts historical trajectories into token-level credit signals under teacher-student conditioning. Experiments on multiple datasets show that CoARS outperforms representative ARS baselines in recommendation performance and user alignment.