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cs.CL2025
WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference
Aiwei Liu, Minghua He, Shaoxun Zeng +7
Autoregressive (AR) generation is the standard decoding paradigm for Large Language Models (LLMs), but its token-by-token nature limits parallelism at inference time. Diffusion Lan…
cs.CL2025
d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models
Leyi Pan, Shuchang Tao, Yunpeng Zhai +8
Reinforcement learning (RL) is pivotal for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, existing dLLM policy optimization methods suffe…
cs.CL2025
WarriorMath: Enhancing the Mathematical Ability of Large Language Models with a Defect-aware Framework
Yue Chen, Minghua He, Fangkai Yang +9
Large Language Models (LLMs) excel in solving mathematical problems, yet their performance is often limited by the availability of high-quality, diverse training data. Existing met…