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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed preprint proposing a multi-objective reinforcement learning framework (MMPO) designed to handle sparse rewards and conflicting objectives in real-world deployment. The authors report improved training stability and better performance across conflicting metrics on e-commerce and other tasks, but the work has not undergone peer review and provides no quantified effect sizes, statistical tests, or comparisons to established baselines in the text provided.
Preprint. Real-world e-commerce datasets, ToolRL, and code generation tasks. Intervention: Multi-Marginal Preference Optimization (MMPO): exposure debiasing, priority-aware orthogonal projection, and self-prompted gradient constraints. Compared with: Traditional linear scalarization.
MMPO improves training stability compared to traditional linear scalarization in handling sparse and biased rewards Framework achieves better performance across conflicting metrics on real-world e-commerce datasets Method generalizes to ToolRL and code generation tasks
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This is an unrefereed arXiv preprint describing a machine learning method with experimental validation on e-commerce and other tasks, but lacks peer review and clinical or human subject evidence.
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Real-world Multi-Objective Reinforcement Learning (MORL) often suffers from sparse rewards, reward conflicts, and late-stage reward tug-of-war, causing traditional linear scalarization to experience severe metric oscillations. To address optimization conflicts among multiple objectives in real-world deployment scenarios, we propose Multi-Marginal Preference Optimization (MMPO), a fine-grained framework that intervenes at the data, gradient, and constraint levels rather than relying on coarse-grained global scalarization. Specifically, MMPO performs exposure debiasing to mitigate sparse and biased rewards, applies priority-aware orthogonal projection to decouple conflicting gradients, and introduces self-prompted gradient constraints to prevent dominant objectives from overwhelming weaker ones. Experiments on real-world e-commerce datasets show that MMPO improves training stability and consistently achieves better performance across conflicting metrics. Moreover, it generalizes robustly to broader tasks such as ToolRL and code generation, demonstrating its effectiveness as a practical paradigm for multi-objective alignment.
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