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Summary	Title & Authors	Links
#1 LGTM Experiments	Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu, Chun Yuan, Mu Li	Github Paper
#2 SCOTT	SCOTT: Self-Consistent Chain-of-Thought Distillation Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, Xiang Ren	Paper
#3 KD-QAT	Understanding and Improving Knowledge Distillation for Quantization Aware Training of Large Transformer Encoders Minsoo Kim, Sihwa Lee, Suk-Jin Hong, Du-Seong Chang, and Jungwook Choi	Github Paper
#4 FlanT5	Specializing Smaller Language Models towards Multi-Step Reasoning Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot	Github Paper
#5 DML Experiments	Deep Mutual Learning Ying Zhang, Tao Xiang, Timothy M. Hospedales, Huchuan Lu	Paper

Summary	Title & Authors	Links
#1 PuMer Evaluation	PuMer: Pruning and Merging Tokens for Efficient Vision Language Models Qingqing Cao, Bhargavi Paranjape, and Hannaneh Hajishirzi	Github Paper
#2 PLMS	Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning Siyu Ren and Kenny Zhu	Github Paper
#3 DPF	Dynamic Model Pruning with Feedback Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi	Paper
#4 GB	Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruning Przemyslaw Joniak and Akiko Aizawa	Github Paper
#5 MP	Movement Pruning: Adaptive Sparsity by Fine-Tuning Victor Sanh1, Thomas Wolf1, Alexander M. Rush1	Github Paper
#6 BMP	Block Pruning For Faster Transformers François Lagunas, Ella Charlaix, Victor Sanh, Alexander Rush	Github Paper