Papers with Touché23-valueEval
V-RoLoRA: RLVR-Driven MoE Routing for Steerable Pluralistic Alignment (2026.findings-acl)
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| Challenge: | Current methods for steering large language models rely on prompt engineering or reasoning-time guidance. |
| Approach: | They propose a value-controllable pluralistic alignment framework enhanced with conditioned gating that dynamically directs the flow among multiple experts based on an input value or moral vector. |
| Outcome: | The proposed method outperforms prompt-based steering and multi-task PEFT benchmarks on two 8-billion-parameter backbones. |