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cs.CL2025
Teaching Your Models to Understand Code via Focal Preference Alignment
Jie Wu, Haoling Li, Xin Zhang +8
Preference learning extends the performance of Code LLMs beyond traditional supervised fine-tuning by leveraging relative quality comparisons. In existing approaches, a set of n ca…
cs.CL2025
Unlocking Multimodal Mathematical Reasoning via Process Reward Model
Ruilin Luo, Zhuofan Zheng, Yifan Wang +9
Process Reward Models (PRMs) have shown promise in enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) through Test-Time Scaling (TTS). However, their…