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。
Zhi Zheng, Rongsheng Chen, Yunpeng Ba +3 authors
Agentic ESOpt uses evolution strategies for scalable full-parameter fine-tuning of long-horizon LLM agents via trajectory-level reward-weighted updates and parameter-context co-evolution.
Zongchuang Zhao, Xin Zhou, Tianyang Xu +5 authors
SimWAM trains a lightweight action planner using video generation as a training signal, enabling efficient trajectory prediction without future generation at inference and supporting reinforcement learning optimization.
Chujie Zheng, Kai Dang, Bowen Yu +7 authors
The paper provides a theoretical foundation for optimizing sequence-level rewards in reinforcement learning using token-level objectives, highlighting the importance of techniques like importance sampling correction, clipping, and Routing Replay for stabilizing training, especially with large language models.
Aarash Feizi, Shravan Nayak, Xiangru Jian +14 authors
GroundCUA, a large-scale desktop grounding dataset, enables the development of GroundNext models that achieve state-of-the-art performance in mapping instructions to UI elements with less training data.
Junying Chen, Zhenyang Cai, Ke Ji +5 authors
The proposed approach enhances medical reasoning in large language models using verifiable problems, guided search, and reinforcement learning to achieve superior performance in medical problem-solving.
Tong Zheng, Hongming Zhang, Wenhao Yu +8 authors
Parallel-R1, a reinforcement learning framework, enhances large language models' reasoning capabilities by enabling parallel thinking through a progressive curriculum, leading to significant performance improvements on math benchmarks.
Xiaoxi Li, Guanting Dong, Jiajie Jin +5 authors
Search-o1 enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module to improve performance on complex reasoning tasks.
Xiaoxi Li, Wenxiang Jiao, Jiarui Jin +8 authors
DeepAgent, an end-to-end deep reasoning agent, autonomously performs thinking, tool discovery, and action execution using memory folding and reinforcement learning, outperforming baselines in various tool-use and application tasks.
Haozhe Wang, Cong Wei, Weiming Ren +3 authors
Training reward models to generate multi-dimensional critiques improves visual generation through both enhanced reinforcement learning rewards and test-time refinement loops, achieving state-of-the-art performance with reduced training data requirements.
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.
Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao +10 authors
AgentOPSD uses recursive Bayesian belief updates over turn-level log-probability gaps to assign credit in long-horizon agentic reinforcement learning without extra critics or rollouts.
Yilei Jiang, Yaozhi Zheng, Yuxuan Wan +4 authors
A modular multi-agent framework named ScreenCoder decomposes UI design-to-code translation into grounding, planning, and generation stages, achieving superior layout accuracy and code correctness through specialized agents and fine-tuned multimodal models.
Yizhou Wang, Chen Tang, Han Deng +29 authors
A scientific reasoning foundation model pre-trained on diverse scientific data supports multiple tasks and enhances cross-domain generalization and fidelity through specialized training techniques.
Weinan Dai, Hanlin Wu, Qiying Yu +13 authors
CUDA Agent, a large-scale agentic reinforcement learning system, achieves state-of-the-art performance in CUDA kernel optimization by combining scalable data synthesis, skill-augmented development environment, and reinforcement learning techniques.
Ling Yang, Ye Tian, Bowen Li +4 authors
MMaDA, a multimodal diffusion foundation model, achieves superior performance through a unified architecture, mixed long chain-of-thought fine-tuning, and a unified policy-gradient-based RL algorithm.
Yiping Wang, Qing Yang, Zhiyuan Zeng +11 authors
Reinforcement learning with verifiable reward using one training example significantly enhances math reasoning capabilities of large language models.
Violet Xiang, Charlie Snell, Kanishk Gandhi +11 authors
The Meta Chain-of-Thought framework extends traditional CoT by explicitly modeling reasoning, enabling more powerful and human-like reasoning in LLMs through process supervision, synthetic data generation, and reinforcement learning.
Yikun Wang, Zuyan Liu, Ziyi Wang +3 authors
GeoVista, an agentic model integrating tool invocation and reinforcement learning, achieves high geolocalization performance on GeoBench, outperforming open-source models and matching closed-source models.
Ibragim Badertdinov, Alexander Golubev, Maksim Nekrashevich +6 authors
A novel pipeline extracts real-world, interactive software engineering tasks from GitHub to create SWE-rebench, improving the evaluation of reinforcement learning models in SWE.
Yunhao Yang, Yuexin Bian, Yunjie Tian +6 authors
Co-RL enables unsupervised reasoning via cooperative multi-agent reinforcement learning with peer-derived rewards, improving performance across text and vision tasks without ground-truth labels.
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