9 papers
VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct
Haoling Li, Kai Zheng, Jie Wu +4
Scaling reinforcement learning for visual mathematical reasoning requires more than generating harder questions: as data volume grows, the reward labels themselves must remain reli…
STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability
Haipeng Luo, Qingfeng Sun, Songli Wu +4
Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from poli…
AgentMath: Empowering Mathematical Reasoning for Large Language Models via Tool-Augmented Agent
Haipeng Luo, Huawen Feng, Qingfeng Sun +6
Large Reasoning Models (LRMs) like o3 and DeepSeek-R1 have achieved remarkable progress in reasoning tasks with long cot. However, they remain computationally inefficient and strug…
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Haipeng Luo, Qingfeng Sun, Can Xu +8
Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, mo…
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Ziyang Luo, Can Xu, Pu Zhao +7
Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on…
WizardLM: Empowering large pre-trained language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng +6
Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming a…