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。
Songchun Zhang, Zeyue Xue, Siming Fu +6 authors
Astrolabe is an efficient online reinforcement learning framework for distilled autoregressive video models that improves generation quality through forward-process RL formulation and streaming training with multi-reward objectives.
Yibin Wang, Zhimin Li, Yuhang Zang +6 authors
Pref-GRPO, a pairwise preference reward-based GRPO method, enhances text-to-image generation by mitigating reward hacking and improving stability, while UniGenBench provides a comprehensive benchmark for evaluating T2I models.
Core Team, Zongming Qiao, Ziyue Hua +146 authors
The MiMo-V2.6 series advances omni-modal foundation models through scaled reinforcement learning with larger batches, diverse agent environments, and robust reward grading, supported by stable training infrastructure.
Zongxia Li, Wenhao Yu, Chengsong Huang +8 authors
Vision-SR1 uses reinforcement learning to enhance visual reasoning in vision-language models by decomposing the process into visual perception and language reasoning stages, improving accuracy and reducing hallucinations.
Jie Wu, Yu Gao, Zilyu Ye +9 authors
RewardDance is a scalable reward modeling framework that aligns with VLM architectures, enabling effective scaling of RMs and resolving reward hacking issues in generation models.
Ganqu Cui, Lifan Yuan, Zefan Wang +20 authors
PRIME leverages implicit process rewards to improve the reinforcement learning of large language models, achieving better performance with less data compared to traditional methods.
Ming Li, Chenguang Wang, Xirui Li +5 authors
Rhetorical framing significantly biases AI scientific review scores in structured ways, with effects shaped by reviewer identity, score range, and evaluation strictness rather than rewriting complexity.
Zhiqing Sun, Sheng Shen, Shengcao Cao +9 authors
The paper presents Factually Augmented RLHF to address multimodal misalignment in large multimodal models, significantly improving hallucination reduction and overall performance on vision-language tasks compared to existing methods.
Shangqing Tu, Daniel Zhang-Li, Yucheng Wang +21 authors
CogEvol is a family of models that generate structured learning artifacts in a single pass using supervised fine-tuning and reinforcement learning with vision-language rewards, achieving high quality with far fewer parameters and lower cost.
Zhiqin Yang, Jingwen Fu, Yuhan Liu +16 authors
This work proposes a structured ladder for scaling large reasoning models beyond human supervision by tracing autonomous rewards and self-generated experience, while identifying risks and evaluation dimensions.
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