21 citations · 57 across the 20 of their papers we have counts for
23 papers
Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability
Xiao Liang, Zhong-Zhi Li, Zhenghao Lin +7
Large language models (LLMs) have demonstrated strong reasoning capabilities through step-by-step chain-of-thought (CoT) reasoning. Nevertheless, at the limits of model capability,…
Gold-Medal-Level Olympiad Geometry Solving with Efficient Heuristic Auxiliary Constructions
Boyan Duan, Xiao Liang, Shuai Lu +7
Automated theorem proving in Euclidean geometry, particularly for International Mathematical Olympiad (IMO) level problems, remains a major challenge and an important research focu…
Mixture of Neuron Experts
Runxi Cheng, Yuchen Guan, Yucheng Ding +6
In this work, we first explore whether the parameters activated by the MoE layer remain highly sparse at inference. We perform a sparsification study on several representative MoE…
Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization
Yaoxiang Wang, Qingguo Hu, Yucheng Ding +6
Mixture-of-Experts (MoE) has emerged as a promising paradigm for efficiently scaling large language models without a proportional increase in computational cost. However, the stand…
Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR
Xiao Liang, Zhongzhi Li, Yeyun Gong +4
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a key paradigm for post-training Large Language Models (LLMs), particularly for complex reasoning task…
PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning
Yizhen Zhang, Yang Ding, Shuoshuo Zhang +9
Inspired by the impressive reasoning capabilities demonstrated by reinforcement learning approaches like DeepSeek-R1, recent emerging research has begun exploring the use of reinfo…
SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning
Xiao Liang, Zhong-Zhi Li, Yeyun Gong +5
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for training large language models (LLMs) on complex reasoning tasks, such as mathematical problem solvin…
Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models
Zhenghao Lin, Zihao Tang, Xiao Liu +31
We introduce Sigma, an efficient large language model specialized for the system domain, empowered by a novel architecture including DiffQKV attention, and pre-trained on our metic…
StreamAdapter: Efficient Test Time Adaptation from Contextual Streams
Dilxat Muhtar, Yelong Shen, Yaming Yang +11
In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks directly from the given demonstrations without requiring gradient updates. While recent advances…
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning
Yaming Yang, Dilxat Muhtar, Yelong Shen +9
Parameter-efficient fine-tuning (PEFT) has been widely employed for domain adaptation, with LoRA being one of the most prominent methods due to its simplicity and effectiveness. Ho…
OmniParser for Pure Vision Based GUI Agent
Yadong Lu, Jianwei Yang, Yelong Shen +1
The recent success of large vision language models shows great potential in driving the agent system operating on user interfaces. However, we argue that the power multimodal model…
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
Liliang Ren, Yang Liu, Yadong Lu +3
Efficiently modeling sequences with infinite context length has long been a challenging problem. Previous approaches have either suffered from quadratic computational complexity or…
Rho-1: Not All Tokens Are What You Need
Zhenghao Lin, Zhibin Gou, Yeyun Gong +8
Previous language model pre-training methods have uniformly applied a next-token prediction loss to all training tokens. Challenging this norm, we posit that "9l training". Our ini…
Exploring the Mystery of Influential Data for Mathematical Reasoning
Xinzhe Ni, Yeyun Gong, Zhibin Gou +4
Selecting influential data for fine-tuning on downstream tasks is a key factor for both performance and computation efficiency. Recent works have shown that training with only limi…
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models
Yi Luo, Zhenghao Lin, Yuhao Zhang +7
Large Language Models (LLMs) exhibit impressive capabilities but also present risks such as biased content generation and privacy issues. One of the current alignment techniques in…
Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning
Yiming Huang, Xiao Liu, Yeyun Gong +4
Large language models (LLMs) have shown great potential in complex reasoning tasks, yet their performance is often hampered by the scarcity of high-quality and reasoning-focused tr…
Multi-LoRA Composition for Image Generation
Ming Zhong, Yelong Shen, Shuohang Wang +6
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated im…
Competition-Level Problems are Effective LLM Evaluators
Yiming Huang, Zhenghao Lin, Xiao Liu +8
Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about these abilities and the potential data contamination problem rec…
Language Models can be Logical Solvers
Jiazhan Feng, Ruochen Xu, Junheng Hao +4
Logical reasoning is a fundamental aspect of human intelligence and a key component of tasks like problem-solving and decision-making. Recent advancements have enabled Large Langua…
Adapting LLM Agents with Universal Feedback in Communication
Kuan Wang, Yadong Lu, Michael Santacroce +3
Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-lingui…
An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
Yadong Lu, Chunyuan Li, Haotian Liu +3
Visual instruction tuning has recently shown encouraging progress with open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However, most existing studies of open…
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving
Zhibin Gou, Zhihong Shao, Yeyun Gong +5
Large language models have made significant progress in various language tasks, yet they still struggle with complex mathematics. In this paper, we propose ToRA a series of Tool-in…
Efficient RLHF: Reducing the Memory Usage of PPO
Michael Santacroce, Yadong Lu, Han Yu +2
Reinforcement Learning with Human Feedback (RLHF) has revolutionized language modeling by aligning models with human preferences. However, the RL stage, Proximal Policy Optimizatio…