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Vector Policy Optimization

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2026-05-22 / arxiv-researcherVector Policy Optimization: Training LLMs for Diversity Improves AlphaEvolve-Style Inference SearchResearchers propose Vector Policy Optimization, which trains LLMs against multiple reward functions simultaneously instead of a single scalar reward. Standard post-training collapses response diversity, hurting inference-time search procedures like AlphaEvolve that need varied rollouts. VPO produces higher-entropy distributions that yield better search performance. Practical for anyone building inference-scaling systems that rely on diverse candidate generation.

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AlphaEvolve
AlphaEvolve inference search benefits from Vector Policy Optimization.
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AlphaEvolve -> Vector Policy Optimization
AlphaEvolve inference search benefits from Vector Policy Optimization.
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