107 citations · 143 across the 45 of their papers we have counts for
27 papers · 2 filters
LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability
Zikai Xiao, Fei Huang, Jianhong Tu +7
Generating long, informative, and factual outputs remains a major challenge for Large Language Models (LLMs). Existing benchmarks for long-form generation typically assess real-wor…
Teaching Language Models to Reason with Tools
Chengpeng Li, Zhengyang Tang, Ziniu Li +8
Large reasoning models (LRMs) like OpenAI-o1 have shown impressive capabilities in natural language reasoning. However, these models frequently demonstrate inefficiencies or inaccu…
Language Confusion Gate: Language-Aware Decoding Through Model Self-Distillation
Collin Zhang, Fei Huang, Chenhan Yuan +1
Large language models (LLMs) often experience language confusion, which is the unintended mixing of languages during text generation. Current solutions to this problem either neces…
Qwen3Guard Technical Report
Haiquan Zhao, Chenhan Yuan, Fei Huang +40
As large language models (LLMs) become more capable and widely used, ensuring the safety of their outputs is increasingly critical. Existing guardrail models, though useful in stat…
Beyond Turn Limits: Training Deep Search Agents with Dynamic Context Window
Qiaoyu Tang, Hao Xiang, Le Yu +11
While recent advances in reasoning models have demonstrated cognitive behaviors through reinforcement learning, existing approaches struggle to invoke deep reasoning capabilities i…
Qwen3-Omni Technical Report
Jin Xu, Zhifang Guo, Hangrui Hu +35
We present Qwen3-Omni, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relat…
Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning
Shaobo Wang, Jiaming Wang, Jiajun Zhang +9
As supervised fine-tuning (SFT) evolves from a lightweight post-training step into a compute-intensive phase rivaling mid-training in scale, data efficiency has become critical for…
RefCritic: Training Long Chain-of-Thought Critic Models with Refinement Feedback
Qiaoyu Tang, Hao Xiang, Le Yu +6
With the rapid advancement of Large Language Models (LLMs), developing effective critic modules for precise guidance has become crucial yet challenging. In this paper, we initially…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs
Baolong Bi, Shenghua Liu, Xingzhang Ren +7
The foundational capabilities of large language models (LLMs) are deeply influenced by the quality of their pre-training corpora. However, enhancing data quality at scale remains a…
RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing
Hao Xiang, Tianyi Tang, Yang Su +10
Recent advancements in Large Language Models (LLMs) have shown outstanding potential for role-playing applications. Evaluating these capabilities is becoming crucial yet remains ch…
CoRT: Code-integrated Reasoning within Thinking
Chengpeng Li, Zhengyang Tang, Ziniu Li +8
Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable progress in natural language reasoning with long chain-of-thought (CoT), yet they remain inefficient or…
Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
Yanzhao Zhang, Mingxin Li, Dingkun Long +9
In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon…
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
Shenzhi Wang, Le Yu, Chang Gao +15
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful approach to enhancing the reasoning capabilities of Large Language Models (LLMs), while its mechanis…
WorldPM: Scaling Human Preference Modeling
Binghai Wang, Runji Lin, Keming Lu +17
Motivated by scaling laws in language modeling that demonstrate how test loss scales as a power law with model and dataset sizes, we find that similar laws exist in preference mode…
Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free
Zihan Qiu, Zekun Wang, Bo Zheng +10
Gating mechanisms have been widely utilized, from early models like LSTMs and Highway Networks to recent state space models, linear attention, and also softmax attention. Yet, exis…
MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation
Xiaoyuan Li, Keqin Bao, Yubo Ma +6
Recent advances in Large Language Models (LLMs) have shown promising results in complex reasoning tasks. However, current evaluations predominantly focus on single-turn reasoning s…
PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts
Yiming Wang, Pei Zhang, Jialong Tang +12
In this paper, we introduce PolyMath, a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels. Our benchmark ensures difficulty c…
START: Self-taught Reasoner with Tools
Chengpeng Li, Mingfeng Xue, Zhenru Zhang +7
Large reasoning models (LRMs) like OpenAI-o1 and DeepSeek-R1 have demonstrated remarkable capabilities in complex reasoning tasks through the utilization of long Chain-of-thought (…
AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models
Qin Zhu, Fei Huang, Runyu Peng +6
While logical reasoning evaluation of Large Language Models (LLMs) has attracted significant attention, existing benchmarks predominantly rely on multiple-choice formats that are v…
DataMan: Data Manager for Pre-training Large Language Models
Ru Peng, Kexin Yang, Yawen Zeng +3
The performance emergence of large language models (LLMs) driven by data scaling laws makes the selection of pre-training data increasingly important. However, existing methods rel…
HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning
Xiaoyuan Li, Moxin Li, Rui Men +6
Large language models (LLMs) have shown remarkable capabilities in commonsense reasoning; however, some variations in questions can trigger incorrect responses. Do these models tru…
Qwen2.5-1M Technical Report
An Yang, Bowen Yu, Chengyuan Li +25
We introduce Qwen2.5-1M, a series of models that extend the context length to 1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series have significantly enha…
RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques
Zhengyang Tang, Ziniu Li, Zhenyang Xiao +8
Critiques are important for enhancing the performance of Large Language Models (LLMs), enabling both self-improvement and constructive feedback for others by identifying flaws and…