1 citations · 1 across the 5 of their papers we have counts for
5 papers
EffiReason-Bench: A Unified Benchmark for Evaluating and Advancing Efficient Reasoning in Large Language Models
Junquan Huang, Haotian Wu, Yubo Gao +7
Large language models (LLMs) with Chain-of-Thought (CoT) prompting achieve strong reasoning but often produce unnecessarily long explanations, increasing cost and sometimes reducin…
EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models
Chengyu Wang, Junbing Yan, Wenrui Cai +2
In this paper, we present EasyDistill, a comprehensive toolkit designed for effective black-box and white-box knowledge distillation (KD) of large language models (LLMs). Our frame…
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models
Chengyu Wang, Junbing Yan, Yuanhao Yue +1
Enhancing computational efficiency and reducing deployment costs for large language models (LLMs) have become critical challenges in various resource-constrained scenarios. In this…
A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions
Chengyu Wang, Taolin Zhang, Richang Hong +1
Recently, the reasoning capabilities of large reasoning models (LRMs), such as DeepSeek-R1, have seen significant advancements through the slow thinking process. Despite these achi…
Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud
Yuanhao Yue, Chengyu Wang, Jun Huang +1
Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specif…