1 citations · 1 across the 10 of their papers we have counts for
11 papers
RL Starts before RL: On Policy Distillation for Better Reinforcement Learning
Shuai Dong, Yongfu Zhu, Yuqi Xu +26
Reinforcement learning (RL) improves reasoning, but its performance depends on the policy from which training begins. We study on-policy distillation (OPD) as a preparation stage f…
SafeRI: Recognition and Intervention for Token-Level Safety Intervention in Large Vision Language Models
Caoyuan Ma, Tian Gu, Wenpu Liu +11
Existing safety alignment methods for vision-language models usually modify the model behavior globally: once the safety parameters are trained or loaded, they participate in both…
SafeCap: Improving LVLM Safety with Image Captioning Reinforcement Learning
Caoyuan Ma, Wenpu Liu, Weichu Xie +12
Large vision-language models (LVLMs) remain vulnerable to jailbreak attacks that exploit visual inputs to bypass safety alignment inherited from their language backbones. We propos…
Leveraging Error Diversity in Group Rollouts for Reinforcement Learning
Wenpu Liu, Yuqi Xu, Weichu Xie +8
Reinforcement Learning from Verifiable Rewards (RLVR) typically samples multiple responses per prompt and assigns binary rewards based on individual correctness, yet the collective…
Right Makes Might: Aligning Verified Hidden States Empowers RL Reasoning
Ziyue Wang, Aomufei Yuan, Yongfu Zhu +10
Reinforcement Learning from Verifiable Rewards (RLVR) has become the dominant approach for improving mathematical reasoning in large language models, yet current methods reduce eac…
SaaS-Bench: Can Computer-Use Agents Leverage Real-World SaaS to Solve Professional Workflows?
Kean Shi, Zihang Li, Tianyi Ma +13
Computer-Using Agents (CUAs) are rapidly extending large language models (LLMs) beyond text-based reasoning toward action execution in more complex environments, such as web browse…
RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades
Xinbo Xu, Ruihan Yang, Haiyang Shen +13
Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many files. However, most existing…
Step-wise Rubric Rewards for LLM Reasoning
Weichu Xie, Haozhe Zhao, Wenpu Liu +15
Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning in large language models, but rewards only final-answer correctness with no supervision ov…
BabyVision: Visual Reasoning Beyond Language
Liang Chen, Weichu Xie, Yiyan Liang +27
While humans develop core visual skills long before acquiring language, contemporary Multimodal LLMs (MLLMs) still rely heavily on linguistic priors to compensate for their fragile…
Multimodal Representation Alignment for Image Generation: Text-Image Interleaved Control Is Easier Than You Think
Liang Chen, Shuai Bai, Wenhao Chai +5
The field of advanced text-to-image generation is witnessing the emergence of unified frameworks that integrate powerful text encoders, such as CLIP and T5, with Diffusion Transfor…
A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation
Liang Chen, Sinan Tan, Zefan Cai +7
This work tackles the information loss bottleneck of vector-quantization (VQ) autoregressive image generation by introducing a novel model architecture called the 2-Dimensional Aut…