1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2025
Efficient Reinforcement Finetuning via Adaptive Curriculum Learning
Taiwei Shi, Yiyang Wu, Linxin Song +2
Reinforcement finetuning (RFT) has shown great potential for enhancing the mathematical reasoning capabilities of large language models (LLMs), but it is often sample- and compute-…
cs.CL2023★ 1 cited
NLPBench: Evaluating Large Language Models on Solving NLP Problems
Linxin Song, Jieyu Zhang, Lechao Cheng +3
Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a…
cs.CV2023
When to Learn What: Model-Adaptive Data Augmentation Curriculum
Chengkai Hou, Jieyu Zhang, Tianyi Zhou
Data augmentation (DA) is widely used to improve the generalization of neural networks by enforcing the invariances and symmetries to pre-defined transformations applied to input d…