2 citations · 5 across the 26 of their papers we have counts for
12 papers · 1 filter
ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas
Xiaoyu Tian, Haotian Wang, Shuaiting Chen +12
Large language models (LLMs) are increasingly used as tool-augmented agents for multi-step decision making, yet training robust tool-using agents remains challenging. Existing meth…
AgriEval: A Comprehensive Chinese Agricultural Benchmark for Large Language Models
Lian Yan, Haotian Wang, Chen Tang +5
In the agricultural domain, the deployment of large language models (LLMs) is hindered by the lack of training data and evaluation benchmarks. To mitigate this issue, we propose Ag…
AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
Yunjie Ji, Xiaoyu Tian, Sitong Zhao +5
We present AM-Thinking-v1, a 32B dense language model that advances the frontier of reasoning, embodying the collaborative spirit of open-source innovation. Outperforming DeepSeek-…
Not All Correct Answers Are Equal: Why Your Distillation Source Matters
Xiaoyu Tian, Yunjie Ji, Haotian Wang +5
Distillation has emerged as a practical and effective approach to enhance the reasoning capabilities of open-source language models. In this work, we conduct a large-scale empirica…
DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Although large language models (LLMs) have recently achieved remarkable performance on various complex reasoning benchmarks, the academic community still lacks an in-depth understa…
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Despite significant advances in long-context reasoning by large language models (LLMs), primarily through Online Reinforcement Learning (RL) methods, these approaches incur substan…
Leveraging Reasoning Model Answers to Enhance Non-Reasoning Model Capability
Haotian Wang, Han Zhao, Shuaiting Chen +5
Recent advancements in large language models (LLMs), such as DeepSeek-R1 and OpenAI-o1, have demonstrated the significant effectiveness of test-time scaling, achieving substantial…
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study
Yunjie Ji, Sitong Zhao, Xiaoyu Tian +5
Enhancing the reasoning capabilities of Large Language Models (LLMs) with efficiency and scalability remains a fundamental challenge in artificial intelligence research. This paper…
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Recent advances in large language models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where extended reasoning processes sub…
1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training
Han Zhao, Haotian Wang, Yiping Peng +5
The AM-DeepSeek-R1-Distilled is a large-scale dataset with thinking traces for general reasoning tasks, composed of high-quality and challenging reasoning problems. These problems…
UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions
Xunzhi Wang, Zhuowei Zhang, Gaonan Chen +7
Despite recent progress in systematic evaluation frameworks, benchmarking the uncertainty of large language models (LLMs) remains a highly challenging task. Existing methods for be…
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Zhen Zhang, Mengting Hu, Shiwan Zhao +6
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER syste…