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。
Shih-Yang Liu, Xin Dong, Ximing Lu +10 authors
Multi-reward reinforcement learning suffers from reward normalization collapse in GRPO, which GDPO addresses by decoupling reward normalization for improved training stability and performance across reasoning tasks.
Changhai Zhou, Kieran Liu, Yuhua Zhou +17 authors
LongStraw is an execution framework that enables million-token reinforcement learning post-training with fixed GPU resources by reducing live training graphs through replay and selective state retention.
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.
Guochao Jiang, Jingyi Song, Guofeng Quan +3 authors
Dynamic Variance-adaptive Advantage Optimization (DVAO) addresses training instability in multi-reward reinforcement learning by adaptively weighting objectives based on empirical reward variance, maintaining bounded advantage magnitudes and improving multi-objective performance.
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.
Sicong Leng, Jing Wang, Jiaxi Li +12 authors
Variance-Aware Sampling and large-scale CoT data improve multimodal reasoning models by stabilizing RL fine-tuning and enhancing performance on benchmarks.
Yibin Wang, Zhimin Li, Yuhang Zang +4 authors
A unified multimodal reward model that incorporates long chains of thought reasoning improves reliability and accuracy in vision reward tasks through exploration-driven reinforcement learning.
Yi Peng, Chris, Xiaokun Wang +12 authors
Skywork R1V extends large language models to multimodal reasoning with efficient transfer, enhanced visual-text alignment, and dynamic reasoning chain optimization, achieving competitive performance in various benchmarks.
Shijian Wang, Jiarui Jin, Xingjian Wang +6 authors
Video-Thinker, a multimodal large language model, autonomously reasons with videos using intrinsic grounding and captioning capabilities, achieving state-of-the-art performance on various video reasoning benchmarks.
Yi-Fan Zhang, Xingyu Lu, Shukang Yin +17 authors
Thyme, a novel paradigm, enables MLLMs to autonomously perform image manipulations and computations, enhancing performance in perception and reasoning tasks through a two-stage training strategy and GRPO-ATS algorithm.
Cong Wan, Zeyu Guo, Zijian Cai +6 authors
Agentic Data Tailoring paradigm uses learnable data processing to structure high-entropy multimodal streams, with DataClaw_0-9B model achieving robust alignment through SFT and GRPO on a novel benchmark.
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.
Yunlong Lin, Zixu Lin, Kunjie Lin +8 authors
JarvisArt, an MLLM-driven agent, achieves superior photo retouching by understanding user intent and coordinating multiple retouching tools in Lightroom, outperforming GPT-4o on a novel benchmark.
Zhengxi Lu, Yuxiang Chai, Yaxuan Guo +5 authors
Rule-based reinforcement learning enhances multimodal large language models for GUI action prediction, improving data efficiency and performance.
Zichen Liu, Changyu Chen, Wenjun Li +5 authors
A study of R1-Zero training identifies pretraining effects on RL performance and proffers Dr. GRPO to enhance token efficiency, achieving superior accuracy on AIME 2024.
Quy-Anh Dang, Chris Ngo
Reinforcement learning enhances reasoning in small language models with limited resources, demonstrating significant performance improvements in mathematical reasoning tasks.
Zhen Fang, Yu Zeng, Wenxuan Huang +17 authors
Video-DeepResearch extends multimodal agents to continuous video via decoupled perception-exploration and GRPO training, achieving state-of-the-art results on a new video reasoning benchmark.
Seohyun Lee, Seoung Choi, Dohwan Ko +2 authors
VideoSearch-R1 is an agentic framework that iteratively retrieves videos and refines search queries using continuous latent space refinement and policy optimization for improved video moment retrieval and temporal grounding.
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