One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems

2026-06-08 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, E-commerce & Digital Commerce · Depth: Expert, quick

Summary

The MORE framework, an adaptive Multi-Objective REinforcement learning system, addresses the challenge of optimizing multiple, complementary objectives in e-commerce dialogue systems, such as reasoning accuracy and linguistic naturalness. Direct mixing of rewards often leads to unstable learning; thus, MORE treats reasoning functions as constraints to guide policy optimization, avoiding additional inference overhead. It also incorporates an adaptive multi-reward mechanism that dynamically reweighs linguistic signals like fluency and naturalness via gradient feedback. Evaluated on two real-world ByteDance dialogue systems and the MultiWOZ 2.2 benchmark, MORE consistently outperformed strong baselines. Online experiments at ByteDance showed a 16.53% improvement in overall conversion, a 30.09% increase in reached conversion, enhanced user satisfaction, and reduced handoff rates, recovering about 60% of the incremental conversion lift achieved by human agents.

Key takeaway

For NLP Engineers or AI Scientists developing e-commerce dialogue systems, you should consider adopting constrained reinforcement learning and adaptive multi-reward mechanisms. This approach, exemplified by MORE, effectively balances complex reasoning with natural language generation, avoiding the instability of directly mixed rewards. Implementing such a framework can significantly improve conversion rates, user satisfaction, and reduce handoff rates in production environments.

Key insights

MORE jointly optimizes reasoning accuracy and linguistic naturalness in e-commerce dialogue systems via constrained reinforcement learning.

Principles

• Treat reasoning as constraints for stable policy optimization.
• Dynamically reweigh linguistic rewards via gradient feedback.
• Avoid direct mixing of diverging optimization dynamics.

Method

MORE uses an adaptive Multi-Objective REinforcement learning framework. It treats reasoning functions as constraints guiding policy optimization and employs an adaptive multi-reward mechanism to dynamically reweigh linguistic signals like fluency and naturalness via gradient feedback.

In practice

• Apply constrained RL for multi-objective dialogue systems.
• Implement adaptive reward reweighing for linguistic goals.
• Integrate reasoning without inference overhead.

Topics

• E-commerce Dialogue Systems
• Multi-Objective Learning
• Reinforcement Learning
• Natural Language Generation
• Reasoning Systems
• Conversion Optimization

Best for: Research Scientist, AI Engineer, AI Product Manager, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.