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。
Yaxuan Li, Yuxin Zuo, Bingxiang He +8 authors
On-policy distillation dynamics in large language models depend on compatible thinking patterns between teacher and student models, with successful distillation characterized by alignment on high-probability tokens and requiring teachers to provide novel capabilities beyond student training data.
Yifan Jiang, Yueying Wang, Rui Zhao +4 authors
VideoP2R, a process-aware reinforcement fine-tuning framework, improves video reasoning and understanding by modeling perception and reasoning separately, achieving state-of-the-art results on multiple benchmarks.
Ximing Lu, David Acuna, Jaehun Jung +12 authors
Golden Goose synthesizes unlimited RLVR tasks from unverifiable internet text by creating multiple-choice question-answering versions of fill-in-the-middle tasks, enabling large-scale training and achieving state-of-the-art results in cybersecurity and other domains.
Shaolei Zhang, Ju Fan, Meihao Fan +2 authors
DeepAnalyze-8B, an agentic LLM, autonomously completes the data science pipeline from raw data to research reports using curriculum-based training and data-grounded trajectory synthesis.
Zhuofeng Li, Haoxiang Zhang, Seungju Han +6 authors
AgentFlow, a trainable agentic framework with in-the-flow optimization, enhances reasoning in large language models by coordinating specialized modules and outperforms top baselines across various tasks.
M-A-P Team, Xinrun Du, Yifan Yao +92 authors
SuperGPQA, a benchmark evaluating LLMs across 285 disciplines, reveals performance gaps and offers insights into the collaborative filtering process with human experts.
Haoran Zhang, Luxin Xu, Zhilin Wang +11 authors
Proactive assistance in personal agent systems requires identifying hidden user intents through sustained multi-turn interactions, which current benchmarks fail to adequately evaluate.
Zhiheng Song, Jingshuai Zhang, Chuan Qin +6 authors
MobileBench is a scalable benchmark for evaluating LLM-based route-planning agents in real-world scenarios, featuring anonymized user queries and a deterministic sandbox for reproducible testing.
Chujie Zheng, Kai Dang, Bowen Yu +7 authors
The paper provides a theoretical foundation for optimizing sequence-level rewards in reinforcement learning using token-level objectives, highlighting the importance of techniques like importance sampling correction, clipping, and Routing Replay for stabilizing training, especially with large language models.
Hugo Laurençon, Léo Tronchon, Matthieu Cord +1 authors
Idefics2, a vision-language model with 8 billion parameters, achieves state-of-the-art performance on multimodal benchmarks through extensive experimental validation.
Renjie Pi, Grace Lam, Mohammad Shoeybi +3 authors
Researchers developed a synthetic task generation pipeline and analyzed data strategies to improve terminal agent performance, creating a large-scale dataset and models that outperform larger counterparts on benchmark tests.
Thaddäus Wiedemer, Yuxuan Li, Paul Vicol +6 authors
Veo 3, a generative video model, exhibits zero-shot capabilities across various visual tasks, suggesting a trajectory towards becoming a unified, generalist vision foundation model.
Weinan Dai, Hanlin Wu, Qiying Yu +13 authors
CUDA Agent, a large-scale agentic reinforcement learning system, achieves state-of-the-art performance in CUDA kernel optimization by combining scalable data synthesis, skill-augmented development environment, and reinforcement learning techniques.
Xun Liang, Jiawei Yang, Yezhaohui Wang +9 authors
SurveyX enhances automated survey generation through innovative preprocessing and post-processing methods, improving content and citation quality beyond existing systems.
Zelai Xu, Zhexuan Xu, Ruize Zhang +7 authors
Multi-agent systems using reinforcement learning enable parallel information seeking with scalable orchestration, achieving performance comparable to larger single agents.
Yiping Wang, Qing Yang, Zhiyuan Zeng +11 authors
Reinforcement learning with verifiable reward using one training example significantly enhances math reasoning capabilities of large language models.
Songhao Wu, Zhongxin Chen, Yuxuan Liu +3 authors
Text embeddings from large language models are enhanced by EmbedFilter, a linear transformation that reduces the influence of high-frequency tokens and improves semantic representations while enabling dimensionality reduction.
Tianyu Fu, Zihan Min, Hanling Zhang +4 authors
Cache-to-Cache (C2C) enables direct semantic communication between LLMs using neural network projections, improving accuracy and reducing latency compared to text-based communication.
Enze Zhang, Jiaying Wang, Mengxi Xiao +7 authors
A new evaluation framework, DITING, and a reasoning-driven multi-agent evaluation framework, AgentEval, are introduced to assess the quality of web novel translations, revealing that Chinese-trained LLMs outperform larger foreign models.
Yuchen Fan, Kaiyan Zhang, Heng Zhou +15 authors
LLMs can serve as efficient simulators for RL tasks by leveraging internal knowledge, reducing reliance on external search engines and improving sim-to-real transfer.
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