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Yitong Yang

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Conference Open access 2026

V-RoLoRA: RLVR-Driven MoE Routing for Steerable Pluralistic Alignment

This work studies value-controllable alignment through discrete condition vectors and proposes Verifiable-reward-Routed LoRA—a parameter-efficient mixture-of-experts LoRA framework enhanced with conditioned gating, which consistently out-performs prompt-based steering and multi-task PEFT baselines.

Jing Wang, Yaomin Wu, Yinglin Wang et al. · 0 citations