Author

Ge Lan

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Jul 2026

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

GTAlign is proposed, a surprisingly simple yet effective Graph-to-Table Alignment framework for text-free Graph Foundation Model, and a community-guided continual pre-training, where pseudo-labels derived from graph community are used to construct few-shot prediction episodes.

Chunyu Hu, Tianyin Liao, Ge Lan et al. · arXiv.org · 0 citations

Jul 2026

Distilled Reinforcement Learning for LLM Post-training

Extensive experiments show that Distilled RL substantially outperforms standard RL and OPD in terms of both pass@1 and pass@k, and can effectively transfer previously unavailable knowledge from a teacher model to a student model.

Chen Wang, Zhaochun Li, Jionghao Bai et al. · arXiv.org · 2 citations