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。
Jeffrey Amico, Gabriel Passamani Andrade, John Donaghy +12 authors
Swarm sAmpling Policy Optimization (SAPO) is a decentralized and asynchronous RL algorithm that enhances post-training language models without supervised fine-tuning, achieving significant reward gains and scalability across diverse hardware.
An Yang, Baosong Yang, Binyuan Hui +55 authors
The Qwen2 series, comprising 0.5 to 72 billion parameter models, surpasses prior open models across language understanding, generation, multilingualism, coding, math, and reasoning, with exceptional performance in benchmarks like MMLU, GPQA, HumanEval, GSM8K, BBH, MT-Bench, Arena-Hard, and LiveCodeBench.
Jiachen Zhu, Xinlei Chen, Kaiming He +2 authors
Dynamic Tanh (DyT) replaces normalization layers in Transformers, achieving equivalent or superior performance without hyperparameter tuning across various tasks.
Shuzheng Si, Haozhe Zhao, Yu Lei +10 authors
A self-evolving framework autonomously discovers and refines context-specific skills for language models through a multi-agent self-play loop with Challenger, Reasoner, and Judge components, improving context learning performance without human supervision.
Zhe Chen, Weiyun Wang, Yue Cao +37 authors
InternVL 2.5, an advanced multimodal large language model, showcases competitive performance across various benchmarks, including multimodal reasoning and understanding, and is the first open-source model to surpass 70% on the MMMU benchmark using Chain-of-Thought reasoning.
Yuxin Zuo, Zikai Xiao, Li Sheng +30 authors
Language-based world models enable agentic environment simulation across multiple domains and enhance general agent performance through scalable simulation and improved downstream task performance.
Linhao Yu, Tianmeng Yang, Siyu Ding +8 authors
KnowRL is a knowledge-guided reinforcement learning framework that improves reasoning in language models by optimizing compact, interaction-aware guidance subsets through constrained subset search and addressing pruning interaction paradoxes.
Maksim Savkin, Mikhail Goncharov, Alexander Gambashidze +7 authors
Compact task-specialized language models demonstrate superior performance in multi-hop reasoning and faithfulness compared to larger general-purpose models through a novel training pipeline and structured reasoning traces.
Jiayi Zhang, Yiran Peng, Fanqi Kong +12 authors
AutoEnv and AutoEnv-36 provide a standardized framework and dataset for evaluating cross-environment learning in agents, highlighting the challenges and limitations of existing learning methods.
Chenkai Pan, Xinglong Xu, Yuhang Xu +6 authors
Training data can be structured as source code and evaluated through unit testing, enabling systematic debugging and improvement of language models' domain-specific capabilities.
Dirk Groeneveld, Iz Beltagy, Pete Walsh +40 authors
OLMo, an open-sourced language model, provides comprehensive access to training data, code, and architecture, facilitating research and innovation in language modeling.
Eric Zelikman, Georges Harik, Yijia Shao +3 authors
Quiet-STaR is an extension of the Self-Taught Reasoner that teaches language models to infer and generate rationales for each token, improving their ability to predict difficult tokens and answer questions without task-specific fine-tuning.
Ilya Gusev
A benchmark evaluates language models' role-playing abilities using automated components to simulate and judge dialogues.
01. AI, Alex Young, Bei Chen +28 authors
The Yi model family, based on transformer architecture, showcases strong performance across benchmarks and modalities through optimized data and scalable infrastructure.
Luca Soldaini, Rodney Kinney, Akshita Bhagia +33 authors
Dolma, a three trillion token English corpus, and its open-source data curation toolkit facilitate open research in language model pretraining by providing detailed insights into data curation practices.
Arindam Mitra, Luciano Del Corro, Guoqing Zheng +11 authors
AgentInstruct, an agentic framework, generates high-quality synthetic data for post-training language models, leading to significant performance improvements across various benchmarks.
Seungone Kim, Juyoung Suk, Xiang Yue +7 authors
AgoraBench evaluates language models' data generation abilities, revealing their distinct strengths and demonstrating various factors that impact effectiveness.
Ronen Eldan, Yuanzhi Li
TinyStories, a synthetic dataset of simplified stories, enables small or simple language models to produce fluent and coherent text with reasoning capabilities using an evaluation framework based on human-like grading.
Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova +5 authors
A novel LLM detector using contrasting scores from two pre-trained models achieves high accuracy in identifying machine-generated text without any training data.
Wei Chen, Zhiyuan Li, Shuo Xin +1 authors
Dolphin, a novel decoder-decoder architecture, enables efficient processing of long contexts in language models by distilling contextual information into memory embeddings, thereby improving energy efficiency and latency without sacrificing accuracy.
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