2 citations · 5 across the 43 of their papers we have counts for
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DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
Zefeng He, Xiaoye Qu, Yafu Li +3
While recent Multimodal Large Language Models (MLLMs) have attained significant strides in multimodal reasoning, their reasoning processes remain predominantly text-centric, leadin…
Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning
Guanjie Chen, Shirui Huang, Kai Liu +5
Diffusion Models have emerged as a leading class of generative models, yet their iterative sampling process remains computationally expensive. Timestep distillation is a promising…
VideoSSR: Video Self-Supervised Reinforcement Learning
Zefeng He, Xiaoye Qu, Yafu Li +3
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially advanced the video understanding capabilities of Multimodal Large Language Models (MLLMs). However, the rapi…
A Survey of Reinforcement Learning for Large Reasoning Models
Kaiyan Zhang, Yuxin Zuo, Bingxiang He +36
In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontie…
Spotlight on Token Perception for Multimodal Reinforcement Learning
Siyuan Huang, Xiaoye Qu, Yafu Li +4
While Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Vision-Language Models (LVLMs), most existing methods in multimodal rea…
ExGRPO: Learning to Reason from Experience
Runzhe Zhan, Yafu Li, Zhi Wang +5
Reinforcement learning from verifiable rewards (RLVR) is an emerging paradigm for improving the reasoning ability of large language models. However, standard on-policy training dis…
FrameThinker: Learning to Think with Long Videos via Multi-Turn Frame Spotlighting
Zefeng He, Xiaoye Qu, Yafu Li +3
While Large Vision-Language Models (LVLMs) have achieved substantial progress in video understanding, their application to long video reasoning is hindered by uniform frame samplin…
Diversity-Incentivized Exploration for Versatile Reasoning
Zican Hu, Shilin Zhang, Yafu Li +7
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a crucial paradigm for incentivizing reasoning capabilities in Large Language Models (LLMs). Due to vast state-…
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Yuhang Cao +173
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou +12
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer m…
SafeWork-R1: Coevolving Safety and Intelligence under the AI-45 Law
Shanghai AI Lab, :, Yicheng Bao +115
We introduce SafeWork-R1, a cutting-edge multimodal reasoning model that demonstrates the coevolution of capabilities and safety. It is developed by our proposed SafeLadder framewo…
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
Zhaochen Su, Peng Xia, Hangyu Guo +12
Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-cent…
Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning
Shuang Chen, Yue Guo, Zhaochen Su +7
Inspired by the remarkable reasoning capabilities of Deepseek-R1 in complex textual tasks, many works attempt to incentivize similar capabilities in Multimodal Large Language Model…
Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models
Yifan Jia, Kailin Jiang, Yuyang Liang +11
Large Multimodal Models(LMMs) face notable challenges when encountering multimodal knowledge conflicts, particularly under retrieval-augmented generation(RAG) frameworks where the…
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning
Zhaochen Su, Linjie Li, Mingyang Song +8
While humans can flexibly leverage interactive visual cognition for complex problem-solving, enabling Large Vision-Language Models (LVLMs) to learn similarly adaptive behaviors wit…
Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
Weigao Sun, Disen Lan, Tong Zhu +2
Linear Sequence Modeling (LSM) like linear attention, state space models and linear RNNs, and Mixture-of-Experts (MoE) have recently emerged as significant architectural improvemen…
SEE: Continual Fine-tuning with Sequential Ensemble of Experts
Zhilin Wang, Yafu Li, Xiaoye Qu +1
Continual fine-tuning of large language models (LLMs) suffers from catastrophic forgetting. Rehearsal-based methods mitigate this problem by retaining a small set of old data. Neve…
Learning to Reason under Off-Policy Guidance
Jianhao Yan, Yafu Li, Zican Hu +5
Recent advances in large reasoning models (LRMs) demonstrate that sophisticated behaviors such as multi-step reasoning and self-reflection can emerge via reinforcement learning wit…
From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration
Mingyang Song, Xiaoye Qu, Jiawei Zhou +1
Large Vision-Language Models (LVLMs) have achieved significant progress in combining visual comprehension with language generation. Despite this success, the training data of LVLMs…
LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid
Weigao Sun, Disen Lan, Yiran Zhong +2
Linear sequence modeling approaches, such as linear attention, provide advantages like linear-time training and constant-memory inference over sequence lengths. However, existing s…
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback
Yafu Li, Xuyang Hu, Xiaoye Qu +2
Large language models (LLMs) demonstrate impressive performance but lack the flexibility to adapt to human preferences quickly without retraining. In this work, we introduce Test-t…
PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models
Mingyang Song, Zhaochen Su, Xiaoye Qu +2
Process-level Reward Models (PRMs) are crucial for complex reasoning and decision-making tasks, where each intermediate step plays an important role in the reasoning process. Since…