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。
Yukang Cao, Haozhe Xie, Fangzhou Hong +4 authors
HSImul3R presents a unified framework for 3D reconstruction of human-scene interactions that bridges the perception-simulation gap through physics-grounded bidirectional optimization and reinforcement learning.
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.
Zhiyuan Hu, Yucheng Wang, Yufei He +7 authors
Reinforcement learning for large language models is enhanced by a rollout-level objective that rewards rare high-level reasoning strategies, improving diverse solution discovery without sacrificing initial performance.
Haozhe Wang, Haoran Que, Qixin Xu +9 authors
REER, a new paradigm for deep reasoning, uses reverse engineering to discover step-by-step reasoning processes, enabling a model to perform competitively on open-ended tasks.
Shiyuan Feng, Huan-ang Gao, Haohan Chi +7 authors
Direct On-Policy Distillation transfers reinforcement learning improvements from smaller to larger models by using the policy shift induced by RL as an implicit reward signal, enabling efficient scaling of training without re-running expensive RL on the target model.
Hongrui Jia, Chaoya Jiang, Shikun Zhang +1 authors
Diagnostic-driven Progressive Evolution enables continuous improvement of large multimodal models through iterative diagnosis and targeted data generation guided by identified weaknesses.
Qiying Yu, Zheng Zhang, Ruofei Zhu +32 authors
The DAPO algorithm, which includes decoupled clip and dynamic sampling policy optimization, enables open-source, high-performance reinforcement learning training for large-scale language models, enhancing reproducibility in the field.
Mingjie Liu, Shizhe Diao, Ximing Lu +5 authors
Prolonged reinforcement learning training (ProRL) uncovers novel reasoning strategies in language models, outperforming base models and suggesting meaningful expansion of reasoning capabilities.
Jiyuan Wang, Chunyu Lin, Lei Sun +8 authors
RL3DEdit uses reinforcement learning with rewards from a 3D foundation model to achieve multi-view consistent 3D editing from 2D editing priors.
Yang Liu, Enxi Wang, Yufei Gao +6 authors
MEDS is a memory-enhanced dynamic reward shaping framework that improves sampling diversity in reinforcement learning for large language models by identifying and penalizing recurrent error patterns through clustering of historical behavioral signals.
Xinyu Geng, Peng Xia, Zhen Zhang +11 authors
WebWatcher, a multimodal agent with enhanced visual-language reasoning, outperforms existing agents in complex visual and textual information retrieval tasks using synthetic trajectories and reinforcement learning.
Qinsi Wang, Bo Liu, Tianyi Zhou +6 authors
Vision-Zero is a domain-agnostic framework that enhances vision-language models through self-improvement in competitive visual games, using Iterative Self-Play Policy Optimization and achieving state-of-the-art performance without human annotation.
Xufang Luo, Yuge Zhang, Zhiyuan He +5 authors
Agent Lightning is a flexible RL framework for training LLMs in various agents, using a hierarchical RL algorithm and decoupling execution from training to handle complex interactions.
Aviral Kumar, Vincent Zhuang, Rishabh Agarwal +15 authors
The multi-turn online reinforcement learning approach SCoRe improves LLM self-correction using self-generated data and regularization, achieving state-of-the-art performance.
Francesco Capuano, Caroline Pascal, Adil Zouitine +2 authors
Robot learning transitions from model-based to data-driven methods, leveraging reinforcement learning and behavioral cloning to develop versatile, language-conditioned models for diverse tasks and robot types.
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.
Xu Wujiang, Wentian Zhao, Zhenting Wang +6 authors
Entropy-regularized Policy Optimization (EPO) addresses exploration-exploitation challenges in multi-turn environments with sparse rewards, improving performance in tasks like ScienceWorld and ALFWorld.
Pengyi Li, Matvey Skripkin, Alexander Zubrey +2 authors
Reinforcement Learning via Self-Confidence (RLSC) improves large language model accuracy using the model's confidence as a reward signal, eliminating the need for human labels or reward engineering.
Fei Tang, Zhangxuan Gu, Zhengxi Lu +9 authors
A new reward framework, GUI-G$^2$, models GUI elements as continuous Gaussian distributions to improve autonomous interaction through dense gradient signals, outperforming existing methods in spatial reasoning tasks.
Yicheng Zou, Dongsheng Zhu, Lin Zhu +171 authors
Intern-S1-Pro is a one-trillion-parameter scientific multimodal foundation model that enhances general and scientific capabilities through advanced agent functionalities and specialized task mastery across multiple scientific disciplines.
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