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。
Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko +5 authors
Efficient inference for large language models on devices with limited DRAM by optimizing data transfer and access from flash memory.
Hao Liang, Xiaochen Ma, Zhou Liu +32 authors
DataFlow is an LLM-driven data preparation framework that enhances data quality and reproducibility for various tasks, improving LLM performance with automatically generated pipelines.
Deepak Nathani, Lovish Madaan, Nicholas Roberts +14 authors
A new framework and benchmark, MLGym and MLGym-Bench, is introduced for evaluating and developing LLM agents across diverse AI research tasks using reinforcement learning.
Wei Huang, Yi Ge, Shuai Yang +11 authors
QeRL, a quantization-enhanced reinforcement learning framework, accelerates RL training for large language models by combining NVFP4 quantization with Low-Rank Adaptation and an Adaptive Quantization Noise mechanism, achieving significant speedups and improved performance.
Yuxin Zuo, Kaiyan Zhang, Shang Qu +7 authors
Test-Time Reinforcement Learning (TTRL) enhances Large Language Models (LLMs) using unlabeled data through reinforcement learning, improving performance across tasks.
Xiao Liang, Zhongzhi Li, Yeyun Gong +4 authors
An online self-play strategy with variational problem synthesis for RLVR training maintains policy entropy and improves Pass@k performance on reasoning benchmarks.
Xuezhi Wang, Denny Zhou
LLMs can perform chain-of-thought reasoning through top-k decoding without manual prompt engineering, outperforming greedy decoding and showing higher confidence in answers.
Gleb Rodionov, Roman Garipov, Alina Shutova +5 authors
A parallel inference engine for LLMs allows concurrent task collaboration through a shared attention cache, improving efficiency without additional fine-tuning.
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan +3 authors
A new 1.3 billion parameter Transformer-based language model, phi-1.5, demonstrates comparable performance to much larger models on common sense reasoning and complex tasks despite the absence of web data.
Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay +38 authors
Introduction to vision-language models (VLMs) covering their applications, training, evaluation, and extension to videos, addressing challenges in mapping visual data to language.
Chengrun Yang, Xuezhi Wang, Yifeng Lu +4 authors
OPRO, a method using large language models to optimize tasks described in natural language, outperforms human-designed prompts on various benchmark datasets.
Advait Gupta, NandaKiran Velaga, Dang Nguyen +1 authors
A three-stage approach, CoSTA*, combines LLMs and A* search to efficiently find cost-effective tool paths for multi-turn text-to-image editing, outperforming state-of-the-art models in cost and quality.
Yang Sui, Yu-Neng Chuang, Guanchu Wang +9 authors
This survey examines progress and methods for improving efficient reasoning in Large Language Models (LLMs), focusing on model, output, and input prompt optimizations, as well as the use of efficient data and small models.
Yumin Choi, Jinheon Baek, Sung Ju Hwang
A meta-learning framework for optimizing system prompts in Large Language Models (LLMs) improves generalization across diverse tasks and datasets.
Zhengyang Tang, Ziniu Li, Zhenyang Xiao +8 authors
SCRIT, a self-evolving critique framework, enhances LLMs' critique capabilities using synthetic data and self-validation, achieving significant improvements in critique-correction and error identification benchmarks.
Tianyu Fu, Yi Ge, Yichen You +6 authors
Roads to Rome (R2R) selectively utilizes large language models for critical reasoning tasks to enhance efficiency and performance in lightweight models.
Junteng Liu, Yuanxiang Fan, Zhuo Jiang +12 authors
SynLogic, a data synthesis framework, enhances the logical reasoning capabilities of Large Language Models through RL, achieving state-of-the-art performance and improving generalization across various domains.
Rujun Han, Yanfei Chen, Zoey CuiZhu +15 authors
TTD-DR, a diffusion-based framework, generates high-quality research reports by iteratively refining a preliminary draft with external information and self-evolutionary algorithms, outperforming existing deep research agents.
Yuzhe Yang, Yifei Zhang, Yan Hu +10 authors
A user-centric framework evaluates LLMs in financial tasks through human feedback and task-specific interactions, achieving high correlation with human preferences.
Zhongyang Li, Ziyue Li, Tianyi Zhou
A novel test-time optimization method, C3PO, improves the accuracy and efficiency of Mixture-of-Experts Large Language Models by re-weighting experts based on surrogate objectives from neighboring samples.
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