6 citations · 56 across the 37 of their papers we have counts for
39 papers
MARS: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation
Pengfei Li, Shijie Wang, Fangyuan Li +7
Reinforcement learning (RL) paradigms have demonstrated strong performance on reasoning-intensive tasks such as code generation. However, limited trajectory diversity often leads t…
SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding
Shuang Cheng, Yuhua Jiang, Zineng Zhou +5
Block-wise discrete diffusion offers an attractive balance between parallel generation and causal dependency modeling, making it a promising backbone for vision-language modeling.…
Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanism
Yuhua Jiang, Shuang Cheng, Yihao Liu +7
Large Language Models (LLMs) excel at general language tasks but struggle in specialized domains. Specialized Generalist Models (SGMs) address this by preserving broad capabilities…
ReviewRL: Towards Automated Scientific Review with RL
Sihang Zeng, Kai Tian, Kaiyan Zhang +9
Peer review is essential for scientific progress but faces growing challenges due to increasing submission volumes and reviewer fatigue. Existing automated review approaches strugg…
OS-MAP: How Far Can Computer-Using Agents Go in Breadth and Depth?
Xuetian Chen, Yinghao Chen, Xinfeng Yuan +12
Computer-using agents have shown strong potential to boost human productivity and enable new application forms across platforms. While recent advances have led to usable applicatio…
GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning
Jian Zhao, Runze Liu, Kaiyan Zhang +8
Recent advancements in Large Language Models (LLMs) have shown that it is promising to utilize Process Reward Models (PRMs) as verifiers to enhance the performance of LLMs. However…
TTRL: Test-Time Reinforcement Learning
Yuxin Zuo, Kaiyan Zhang, Li Sheng +13
This paper investigates Reinforcement Learning (RL) on data without explicit labels for reasoning tasks in Large Language Models (LLMs). The core challenge of the problem is reward…
Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling
Runze Liu, Junqi Gao, Jian Zhao +5
Test-Time Scaling (TTS) is an important method for improving the performance of Large Language Models (LLMs) by using additional computation during the inference phase. However, cu…
Process Reinforcement through Implicit Rewards
Ganqu Cui, Lifan Yuan, Zefan Wang +22
Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in ta…
Free Process Rewards without Process Labels
Lifan Yuan, Wendi Li, Huayu Chen +6
Different from its counterpart outcome reward models (ORMs), which evaluate the entire responses, a process reward model (PRM) scores a reasoning trajectory step by step, providing…
How to Synthesize Text Data without Model Collapse?
Xuekai Zhu, Daixuan Cheng, Hengli Li +7
Model collapse in synthetic data indicates that iterative training on self-generated data leads to a gradual decline in performance. With the proliferation of AI models, synthetic…
Evolution of Thought: Diverse and High-Quality Reasoning via Multi-Objective Optimization
Biqing Qi, Zhouyi Qian, Yiang Luo +4
As multi-modal large language models (MLLMs) are increasingly applied to complex reasoning tasks, the diversity and quality of reasoning paths become crucial factors affecting thei…
Automating Exploratory Proteomics Research via Language Models
Ning Ding, Shang Qu, Linhai Xie +13
With the development of artificial intelligence, its contribution to science is evolving from simulating a complex problem to automating entire research processes and producing nov…
Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention
Xingtai Lv, Ning Ding, Kaiyan Zhang +3
Improving the effectiveness and efficiency of large language models (LLMs) simultaneously is a critical yet challenging research goal. In this paper, we find that low-rank pre-trai…
MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for Superior Planning and Decision-Making
Dayuan Fu, Biqing Qi, Yihuai Gao +3
Long-term memory is significant for agents, in which insights play a crucial role. However, the emergence of irrelevant insight and the lack of general insight can greatly undermin…
SR-CIS: Self-Reflective Incremental System with Decoupled Memory and Reasoning
Biqing Qi, Junqi Gao, Xinquan Chen +3
The ability of humans to rapidly learn new knowledge while retaining old memories poses a significant challenge for current deep learning models. To handle this challenge, we draw…
Neural Residual Diffusion Models for Deep Scalable Vision Generation
Zhiyuan Ma, Liangliang Zhao, Biqing Qi +1
The most advanced diffusion models have recently adopted increasingly deep stacked networks (e.g., U-Net or Transformer) to promote the generative emergence capabilities of vision…
Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation
Biqing Qi, Kaiyan Zhang, Kai Tian +6
The rapid growth of biomedical knowledge has outpaced our ability to efficiently extract insights and generate novel hypotheses. Large language models (LLMs) have emerged as a prom…
Towards Building Specialized Generalist AI with System 1 and System 2 Fusion
Kaiyan Zhang, Biqing Qi, Bowen Zhou
In this perspective paper, we introduce the concept of Specialized Generalist Artificial Intelligence (SGAI or simply SGI) as a crucial milestone toward Artificial General Intellig…
Online DPO: Online Direct Preference Optimization with Fast-Slow Chasing
Biqing Qi, Pengfei Li, Fangyuan Li +3
Direct Preference Optimization (DPO) improves the alignment of large language models (LLMs) with human values by training directly on human preference datasets, eliminating the nee…
SMR: State Memory Replay for Long Sequence Modeling
Biqing Qi, Junqi Gao, Kaiyan Zhang +4
Despite the promising performance of state space models (SSMs) in long sequence modeling, limitations still exist. Advanced SSMs like S5 and S6 (Mamba) in addressing non-uniform sa…
Fast and Slow Generating: An Empirical Study on Large and Small Language Models Collaborative Decoding
Kaiyan Zhang, Jianyu Wang, Ning Ding +4
Large Language Models (LLMs) exhibit impressive capabilities across various applications but encounter substantial challenges such as high inference latency, considerable training…
Exploring Adversarial Robustness of Deep State Space Models
Biqing Qi, Yang Luo, Junqi Gao +4
Deep State Space Models (SSMs) have proven effective in numerous task scenarios but face significant security challenges due to Adversarial Perturbations (APs) in real-world deploy…
UltraMedical: Building Specialized Generalists in Biomedicine
Kaiyan Zhang, Sihang Zeng, Ermo Hua +11
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas. Recent advanced proprietary models such…