YFPO: Yoked Feature Preference Optimization with Neuron-Guided Rewards
Preference optimization has become a widely used post-training paradigm for improving the reasoning abilities of large language models. Existing methods typically learn from preferred and dispreferred responses as external behavioral supervision, while largely ignoring capability-related signals encoded in the model's internal representations. In this work, we study whether such internal signals can provide useful auxiliary supervision for mathematical reasoning. We introduce YFPO (Yoked Feature Preference Optimization), a neuron-guided preference optimization framework that couples response-level preference learning with neuron-level rewards. YFPO first uses AttnLRP to identify math-asso...
| Authors | Yifan Le |
|---|---|
| Primary category | cs.CL |
| Published | 12 May 2026 |
| Last updated at source | 15 September 2026 |
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