1 citations · 5 across the 31 of their papers we have counts for
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Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model
Ling Team, Anqi Shen, Baihui Li +101
We present Ring-1T, the first open-source, state-of-the-art thinking model with a trillion-scale parameter. It features 1 trillion total parameters and activates approximately 50 b…
SpatialLadder: Progressive Training for Spatial Reasoning in Vision-Language Models
Hongxing Li, Dingming Li, Zixuan Wang +7
Spatial reasoning remains a fundamental challenge for Vision-Language Models (VLMs), with current approaches struggling to achieve robust performance despite recent advances. We id…
EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering
Haolei Xu, Xinyu Mei, Yuchen Yan +5
Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a…
GSM8K-V: Can Vision Language Models Solve Grade School Math Word Problems in Visual Contexts
Fan Yuan, Yuchen Yan, Yifan Jiang +9
Mathematical reasoning is a key capability for vision-language models (VLMs), yet current benchmarks mainly evaluate text-based or explicitly symbolic visual inputs. It remains unc…
LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization
Xingyu Wu, Yuchen Yan, Shangke Lyu +7
Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for…
Cooper: Co-Optimizing Policy and Reward Models in Reinforcement Learning for Large Language Models
Haitao Hong, Yuchen Yan, Xingyu Wu +5
Large language models (LLMs) have demonstrated remarkable performance in reasoning tasks, where reinforcement learning (RL) serves as a key algorithm for enhancing their reasoning…
Hierarchical Budget Policy Optimization for Adaptive Reasoning
Shangke Lyu, Linjuan Wu, Yuchen Yan +7
Large reasoning models achieve remarkable performance through extensive chain-of-thought generation, yet they suffer from a critical inefficiency: applying uniformly extensive reas…
Test-Time Reinforcement Learning for GUI Grounding via Region Consistency
Yong Du, Yuchen Yan, Fei Tang +5
Graphical User Interface (GUI) grounding, the task of mapping natural language instructions to precise screen coordinates, is fundamental to autonomous GUI agents. While existing m…
GroundAct: Can LLM Agents Ground Actions in Environmental States?
Zixuan Wang, Dingming Li, Hongxing Li +8
LLM agents achieve 85-96% success on tasks where instructions fully specify the action, but drop to 29-53% when action feasibility depends on environmental state that the instructi…
SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
Siqi Chen, Xinyu Dong, Haolei Xu +10
Large Language Models (LLMs) and Multimodal LLMs have shown promising capabilities for SVG processing, yet existing benchmarks suffer from limited real-world coverage, lack of comp…
Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuning
Xin Xu, Tianhao Chen, Fan Zhang +11
While slow-thinking large language models (LLMs) exhibit reflection-like reasoning, commonly referred to as the "aha moment:, their ability to generate informative critiques and re…
Do Large Language Models Excel in Complex Logical Reasoning with Formal Language?
Jin Jiang, Jianing Wang, Yuchen Yan +5
Large Language Models (LLMs) have been shown to achieve breakthrough performance on complex logical reasoning tasks. Nevertheless, most existing research focuses on employing forma…
ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models
Dingming Li, Hongxing Li, Zixuan Wang +9
Vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and reasoning about visual content, but significant challenges persist in tasks requiring c…
VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models
Yuchen Yan, Jin Jiang, Zhenbang Ren +9
Large reasoning models such as OpenAI o1 and DeepSeek-R1 have demonstrated remarkable performance in complex reasoning tasks. A critical component of their training is the incorpor…
Let LRMs Break Free from Overthinking via Self-Braking Tuning
Haoran Zhao, Yuchen Yan, Yongliang Shen +7
Large reasoning models (LRMs), such as OpenAI o1 and DeepSeek-R1, have significantly enhanced their reasoning capabilities by generating longer chains of thought, demonstrating out…
A Survey on (M)LLM-Based GUI Agents
Fei Tang, Haolei Xu, Hang Zhang +12
Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-drive…
InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models
Yuchen Yan, Yongliang Shen, Yang Liu +4
Advanced reasoning in large language models has achieved remarkable performance on challenging tasks, but the prevailing long-context reasoning paradigm faces critical limitations:…
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification
Xuan Zhang, Yongliang Shen, Zhe Zheng +6
Large language models (LLMs) have demonstrated remarkable capabilities in tool learning. In real-world scenarios, user queries are often ambiguous and incomplete, requiring effecti…
Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving
Xin Xu, Yan Xu, Tianhao Chen +9
Existing approaches to mathematical reasoning with large language models (LLMs) rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated Reasoning (TIR) for precise c…
MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps through Fill-in-the-Middle Task
Yuchen Yan, Yongliang Shen, Yang Liu +5
Mathematical reasoning represents a critical frontier in advancing large language models (LLMs). While step-by-step approaches have emerged as the dominant paradigm for mathematica…
UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models
Xin Xu, Qiyun Xu, Tong Xiao +6
Large language models (LLMs) have demonstrated remarkable capabilities in solving complex reasoning tasks, particularly in mathematics. However, the domain of physics reasoning pre…