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。
Jiahao Zhao, Xiaomin Yu, Zhongxiang Sun +5 authors
ToolArtist is an agentic image generation model that unifies reasoning, tool use, and image synthesis under a single policy via supervised fine-tuning and reinforcement learning.
Lingfei Qian, Weipeng Zhou, Yan Wang +3 authors
A study evaluates 16 large language models on complex financial tasks, finding that domain-specific CoT fine-tuning and reinforcement learning improve performance and highlight the need for further research on long-context and multi-table reasoning.
Junhao Shen, Zhanqiu Zhang, Yiwen Guo +1 authors
Skill-α uses reinforcement learning with rollback rewards to progressively generate agent skills by evaluating sequential edits against downstream task performance.
Zijun Liu, Peiyi Wang, Runxin Xu +5 authors
Self-Principled Critique Tuning enhances pointwise generative reward modeling for large language models, improving scalability and quality compared to existing methods.
Lin Long, Yichen He, Wentao Ye +5 authors
M3-Agent, a multimodal agent with long-term memory, performs multi-turn reasoning and outperforms baselines on a new long-video question answering benchmark.
Edward Yeo, Yuxuan Tong, Morry Niu +2 authors
Investigation into long chains-of-thought reasoning in large language models reveals the critical role of training compute, reward shaping, and verifiable reward signals in enabling and measuring this capability.
Yuhao Wu, Yushi Bai, Zhiqiang Hu +2 authors
An incentivization-based reinforcement learning approach is used to develop a large language model capable of generating ultra-long, high-quality text without the need for synthetic data or supervised fine-tuning.
Hengguang Zhou, Xirui Li, Ruochen Wang +3 authors
A non-SFT model replicated emergent reasoning characteristics for multimodal tasks using reinforcement learning, achieving higher accuracy than base and SFT models on CVBench.
Chengqi Lyu, Songyang Gao, Yuzhe Gu +14 authors
A novel reinforcement learning framework, OREAL, using outcome rewards achieves high accuracy in mathematical reasoning tasks with smaller models compared to larger models trained using distillation.
Qi Sun, Edoardo Cetin, Yujin Tang
A self-adaptive framework for large language models uses reinforcement learning to dynamically adjust task-specific components during inference, enhancing adaptability and performance with efficiency.
Kejian Zhu, Zhuoran Jin, Shangqing Tu +5 authors
Reinforcement learning enables stable multi-task reasoning in large language models through sparse, near-orthogonal parameter updates, unlike supervised fine-tuning which suffers from task conflicts, motivating a decoupled Parallel-RL training approach.
Yichen He, Guanhua Huang, Peiyuan Feng +4 authors
A quality Paper Search agent, PaSa, uses reinforcement learning on synthetic and real academic queries, outperforming Google and GPT-4-based systems.
Quy-Anh Dang, Chris Ngo
Reinforcement learning enhances reasoning in small language models with limited resources, demonstrating significant performance improvements in mathematical reasoning tasks.
Jiale Kang, Ziyin Yue, Zheng Zhan +2 authors
Normalized Low-Rank Adaptation stabilizes LoRA training by normalizing down-projection matrices, accelerating convergence and improving performance without extra parameters or inference cost.
Zhilin Wang, Han Song, Runzhe Zhan +13 authors
Autonomous agents evaluate policy improvement through iterative editing within fixed budgets, revealing that successful policy evolution requires both task-specific mechanisms and feedback-constrained refinement.
Junyao Yang, Yucheng Shi, Zhongzhi Li +4 authors
T1 is a 122B Mixture-of-Experts model trained with reinforcement learning to execute long-horizon terminal tasks in a cloud sandbox, achieving state-of-the-art results through stable actor-critic optimization and out-of-distribution training.
Yuhan Wang, Zhengxi Lu, Yuchen Yan +6 authors
PaperGym converts scientific papers into training environments by separating research questions from evaluation rubrics, enabling reinforcement learning that improves research planning across multiple model sizes.
Young-Jun Lee, Seungone Kim, Minki Kang +5 authors
Evolutionary fine-tuning enables large language models to develop cross-task problem-solving capabilities by learning from search trajectories, demonstrating improved performance on mathematical conjectures and optimization tasks.
Di Zhang, Jingdi Lei, Junxian Li +10 authors
The Critic-V framework leverages an Actor-Critic paradigm to enhance multimodal reasoning in vision-language models by decoupling reasoning and critique processes, significantly improving performance and reliability.
Philip Anastassiou, Jiawei Chen, Jitong Chen +43 authors
Seed-TTS is a family of large-scale TTS models that generate high-quality speech with in-context learning, superior controllability, and a non-autoregressive variant using diffusion-based architecture that does not rely on pre-estimated phoneme durations.
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