3 citations · 3 across the 3 of their papers we have counts for
6 papers
Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series
Wenrui Cai, Chengyu Wang, Junbing Yan +2
Recently, the demand for small and efficient reasoning models to support real-world applications has driven the development of knowledge distillation techniques that balance reason…
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…
Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations
Wenrui Cai, Chengyu Wang, Junbing Yan +2
The emergence of large reasoning models (LRMs) has transformed Natural Language Processing by excelling in complex tasks such as mathematical problem-solving and code generation. T…
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…
Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment
Wenrui Cai, Chengyu Wang, Junbing Yan +2
The reasoning capabilities of large reasoning models (LRMs), such as OpenAI's o1 and DeepSeek-R1, have seen substantial advancements through deep thinking. However, these enhanceme…
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper
Chengyu Wang, Junbing Yan, Wei Zhang +1
This paper delves into the pressing need in Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models (LLMs). While LLMs possess remarkable capabilities, their extensive par…