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。
Tiwei Bie, Maosong Cao, Xiang Cao +47 authors
LLaDA2.1 introduces a novel token-to-token editing approach with speed and quality modes, enhanced through reinforcement learning for improved reasoning and instruction following in large language diffusion models.
Egor Cherepanov, Nikita Kachaev, Artem Zholus +2 authors
The paper provides a framework for understanding and evaluating different types of memory in reinforcement learning agents, using cognitive science-inspired definitions and a standardized experimental methodology.
Mistral-AI, Abhinav Rastogi, Albert Q. Jiang +97 authors
Magistral, a scalable reinforcement learning pipeline, demonstrates that RL can enhance multimodal understanding and instruction following in large language models without requiring existing RL traces.
OpenAI, Ahmed El-Kishky, Alexander Wei +22 authors
General-purpose reinforcement learning applied to large language models outperforms domain-specific systems in complex coding and reasoning tasks, achieving top results in competitions without hand-crafted strategies.
Baorong Shi, Bo Cui, Boyuan Jiang +17 authors
MedXIAOHE is a medical vision-language foundation model that enhances clinical understanding through entity-aware continual pretraining, reinforcement learning, and tool-augmented agentic training for reliable diagnostic reasoning.
Kang Chen, Zhihao Liu, Tonghe Zhang +10 authors
The framework π<sub>RL</sub> uses reinforcement learning to train flow-based Vision-Language-Action models, addressing challenges with intractable action log-likelihoods and achieving significant performance improvements over supervised fine-tuning.
Hao Sun, Zile Qiao, Jiayan Guo +6 authors
ZeroSearch is a reinforcement learning framework that improves LLM search capabilities without using real search engines, using supervised fine-tuning and curriculum-based rollouts to handle document quality and API cost challenges.
Yuqiao Tan, Minzheng Wang, Shizhu He +6 authors
The paper decomposes the policy of large language models into internal layer and modular policies, revealing distinct reasoning patterns across layers and proposing Bottom-up Policy Optimization to enhance performance on complex reasoning tasks.
Xun Liang, Hanyu Wang, Yezhaohui Wang +8 authors
Controllable Text Generation techniques for Large Language Models ensure predefined control conditions and high-quality text output, covering content and attribute control through various methods like retraining, fine-tuning, and latent manipulation.
Yijun Lu, Rui Ye, Jiajun Wang +4 authors
Answer-Backtracked Credit Assignment improves long-horizon search agents by assigning dense step-level rewards via answer-backtracked clue recovery and clue-anchored scoring, yielding strong benchmark results with small models.
Yan Ma, Linge Du, Xuyang Shen +7 authors
A unified reinforcement learning system, V-Triune, combines visual reasoning and perception tasks in vision-language models through a single training pipeline, achieving significant improvements across various tasks.
Tingchen Fu, Jiawei Gu, Yafu Li +2 authors
An empirical analysis of MathIF identifies a tension between enhancing reasoning capacity and maintaining instruction adherence in large language models.
Jiazhan Feng, Shijue Huang, Xingwei Qu +6 authors
ReTool, a tool-integrated learning framework, enhances reasoning models with real-time code execution and reinforcement learning, significantly improving performance in structured problem-solving tasks like mathematical reasoning.
Ziyuan Liu, Hengqi Liu, Zichuan Wang +6 authors
Two large-scale search agents are trained via a multi-stage pipeline combining supervised fine-tuning and reinforcement learning against live search, achieving state-of-the-art open-source results on complex web benchmarks through rigorous trajectory filtering and inference-time context management.
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.
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.
Jiazhen Pan, Che Liu, Junde Wu +6 authors
MedVLM-R1 enhances medical image analysis through explicit natural language reasoning generation, achieving superior accuracy with limited data and parameters via reinforcement learning rather than supervised fine-tuning.
Nuo Chen, Zhiyuan Hu, Qingyun Zou +4 authors
JudgeLRM models trained with reinforcement learning outperform existing models, especially in tasks requiring deep reasoning.
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.
Hakim Sidahmed, Samrat Phatale, Alex Hutcheson +16 authors
Parameter-efficient reinforcement learning using LoRA in RLHF matches conventional RLHF performance with reduced computational cost and memory usage.
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