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Jianguo Li

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Preprint Aug 2026

CForce: Boosting Parallel Decoding for dLLMs via Consistency Forcing

This work presents Consistency Forcing (CForce) for dLLMs, a distillation method to force the mask predictions of early stages to align with those of later stages, thereby improving training-inference alignment.

Yujie Ren, Chenkai Xu, Zhuocheng Gong et al. · 0 citations
Book Open access Aug 2026

Domain-Specific Data Synthesis for LLMs through Minimal Sufficient Representation Learning

This work proposes a novel framework, DOMINO, that learns a minimal sufficient domain representation from reference samples and leverages it to guide the generation of domain-aligned synthetic data, enabling practical and scalable domain adaptation without manual prompt design or natural language domain specifications.

Tong Ye, Hang Yu, Tengfei Ma et al. · 1 citation