Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
An Yang, Anfeng Li, Baosong Yang +57 authors
Qwen3, a unified series of large language models, integrates thinking and non-thinking modes, reduces computational resources, and achieves state-of-the-art performance across various tasks and languages.
Fengkai Yang, Zherui Chen, Xiaohan Wang +10 authors
Group-based reinforcement learning from verifier rewards suffers from biased advantage estimation that underestimates hard prompts and overestimates easy prompts, which is addressed through a history-aware adaptive difficulty weighting method that improves performance on mathematical reasoning benchmarks.
Zhihong Shao, Peiyi Wang, Qihao Zhu +6 authors
DeepSeekMath 7B improves mathematical reasoning through enhanced data pre-training and Group Relative Policy Optimization, achieving high scores on MATH benchmark without external tools.
Runqi Qiao, Qiuna Tan, Peiqing Yang +11 authors
We-Math 2.0 enhances MLLMs' mathematical reasoning through a structured knowledge system, model-centric data space modeling, and reinforcement learning, demonstrating competitive performance on benchmarks.
Kimi Team, Angang Du, Bohong Yin +89 authors
Kimi-VL, an efficient Mixture-of-Experts vision-language model, excels in multimodal reasoning, long-context understanding, and diverse vision-language tasks, achieving competitive performance with reduced computational cost.
Xingxuan Li, Yao Xiao, Dianwen Ng +15 authors
The MiroMind-M1 series, built on Qwen-2.5, introduces fully open-source reasoning language models with state-of-the-art performance in mathematical reasoning through Context-Aware Multi-Stage Policy Optimization.
Weizhen Li, Jianbo Lin, Zhuosong Jiang +27 authors
Chain-of-Agents (CoA) paradigm enables end-to-end complex problem-solving in LLMs through dynamic agent activation, improving performance via multi-agent distillation and agentic reinforcement learning.
Yanqi Dai, Yuxiang Ji, Xiao Zhang +3 authors
MathForge enhances mathematical reasoning in large models through a dual framework combining difficulty-aware policy optimization and multi-aspect question reformulation to address limitations in existing reinforcement learning methods.
Peng Xia, Kaide Zeng, Jiaqi Liu +5 authors
Agent0, a self-evolving framework utilizing multi-step co-evolution and tool integration, enhances LLM reasoning capabilities without human-curated data.
Xin Chan, Xiaoyang Wang, Dian Yu +2 authors
Persona-driven synthetic data creation using a large language model and 1 billion diverse personas for various applications demonstrates versatility, scalability, and flexibility.
Zhihong Shao, Yuxiang Luo, Chengda Lu +6 authors
A self-verifying large language model for theorem proving improves mathematical reasoning by incentivizing rigorous step-by-step derivations and achieves high scores in international competitions.
Zixun Huang, Kishan Panaganti, Haitao Mi +1 authors
FlowBalance improves reasoning models via verifier-calibrated self-guidance using trajectory-level score reweighting and profile-based trajectory balance.
Shuming Ma, Hongyu Wang, Shaohan Huang +5 authors
BitNet b1.58 2B4T, a 1-bit Large Language Model with 2 billion parameters, matches the performance of full-precision models while improving computational efficiency.
Chujie Zheng, Zhenru Zhang, Beichen Zhang +6 authors
ProcessBench evaluates models' ability to identify errors in mathematical reasoning steps, showing that existing process reward models struggle with difficult problems and underperform compared to critic models and a fine-tuned PRM.
Dongfu Jiang, Yi Lu, Zhuofeng Li +9 authors
VerlTool is a unified and modular framework for Agentic Reinforcement Learning with Tool use, addressing inefficiencies in existing approaches and providing competitive performance across multiple domains.
Zanlin Ni, Shenzhi Wang, Yang Yue +8 authors
Arbitrary order generation in diffusion large language models limits reasoning capability by causing premature solution space collapse, making standard policy optimization more effective.
DeepSeek-AI, Qihao Zhu, Daya Guo +37 authors
DeepSeek-Coder-V2, a Mixture-of-Experts language model, excels in code-specific tasks by enhancing coding and mathematical reasoning capabilities while expanding language support and context length.
Wenxuan Zhang, Hou Pong Chan, Yiran Zhao +9 authors
SeaLLMs 3 is a large language model optimized for Southeast Asian languages, offering state-of-the-art performance in various tasks while addressing safety and cultural considerations.
Quy-Anh Dang, Chris Ngo
Reinforcement learning enhances reasoning in small language models with limited resources, demonstrating significant performance improvements in mathematical reasoning tasks.
Haoran Li, Qingxiu Dong, Zhengyang Tang +17 authors
GLAN, a general method for instruction tuning of LLMs, uses a taxonomy of human knowledge to generate synthetic instruction data, achieving strong performance across diverse tasks without task-specific training data.
北京市昌平区探索星信息技术及软件开发工作室
京ICP备2026059466号