1 citations · 1 across the 9 of their papers we have counts for
9 papers
LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling
Jian Yang, Shawn Guo, Wei Zhang +16
Looped Transformers scale latent computation by repeatedly applying shared blocks, but sequential looping increases latency and KV-cache memory with the loop count. Parallel loop T…
Improving Vision-language Models with Perception-centric Process Reward Models
Yingqian Min, Kun Zhou, Yifan Li +6
Recent advancements in reinforcement learning with verifiable rewards (RLVR) have significantly improved the complex reasoning ability of vision-language models (VLMs). However, it…
InCoder-32B-Thinking: Industrial Code World Model for Thinking
Jian Yang, Wei Zhang, Jiajun Wu +22
Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and…
Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization
Fei Bai, Zhipeng Chen, Chuan Hao +6
Recently, reinforcement learning~(RL) has become an important approach for improving the capabilities of large language models~(LLMs). In particular, reinforcement learning from ve…
Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning
Yifan Li, Yukai Gu, Yingqian Min +6
Recent breakthroughs in video generation have demonstrated an emerging capability termed Chain-of-Frames (CoF) reasoning, where models resolve complex tasks through the generation…
Revisiting the Necessity of Lengthy Chain-of-Thought in Vision-centric Reasoning Generalization
Yifan Du, Kun Zhou, Yingqian Min +3
We study how different Chain-of-Thought (CoT) designs affect the acquisition of the generalizable visual reasoning ability in vision-language models (VLMs). While CoT data, especia…
Unleashing Perception-Time Scaling to Multimodal Reasoning Models
Yifan Li, Zhenghao Chen, Ziheng Wu +7
Recent advances in inference-time scaling, particularly those leveraging reinforcement learning with verifiable rewards, have substantially enhanced the reasoning capabilities of L…
UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
Haoming Wang, Haoyang Zou, Huatong Song +109
The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…
Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models
Zhipeng Chen, Xiaobo Qin, Youbin Wu +4
Reinforcement learning with verifiable rewards (RLVR), which typically adopts Pass@1 as the reward, has faced the issues in balancing exploration and exploitation, causing policies…