2 citations · 2 across the 9 of their papers we have counts for
10 papers
Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement
Yi Ding, Ruqi Zhang
On-policy distillation (OPD) offers dense token-level supervision as an alternative to the sparse outcome-level advantages of reinforcement learning with verifiable rewards (RLVR).…
Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control
Bolian Li, Yifan Wang, Yi Ding +3
Reinforcement learning (RL) has enabled complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performance saturation, preventing cont…
Modular Safety Guardrails Are Necessary for Foundation-Model-Enabled Robots in the Real World
Joonkyung Kim, Wenxi Chen, Davood Soleymanzadeh +9
The integration of foundation models (FMs) into robotics has accelerated real-world deployment, while introducing new safety challenges arising from open-ended semantic reasoning a…
Learning Self-Correction in Vision-Language Models via Rollout Augmentation
Yi Ding, Ziliang Qiu, Bolian Li +1
Self-correction is essential for solving complex reasoning problems in vision-language models (VLMs). However, existing reinforcement learning (RL) methods struggle to learn it, as…
ForeDiffusion: Foresight-Conditioned Diffusion Policy via Future View Construction for Robot Manipulation
Weize Xie, Yi Ding, Ying He +5
Diffusion strategies have advanced visual motor control by progressively denoising high-dimensional action sequences, providing a promising method for robot manipulation. However,…
How Far Are Surgeons from Surgical World Models? A Pilot Study on Zero-shot Surgical Video Generation with Expert Assessment
Zhen Chen, Qing Xu, Jinlin Wu +7
Foundation models in video generation are demonstrating remarkable capabilities as potential world models for simulating the physical world. However, their application in high-stak…
ViTSP: A Vision Language Models Guided Framework for Solving Large-Scale Traveling Salesman Problems
Zhuoli Yin, Yi Ding, Reem Khir +1
Solving the Traveling Salesman Problem (TSP) is NP-hard yet fundamental for a wide range of real-world applications. Classical exact methods face challenges in scaling, and heurist…
Sherlock: Self-Correcting Reasoning in Vision-Language Models
Yi Ding, Ruqi Zhang
Reasoning Vision-Language Models (VLMs) have shown promising performance on complex multimodal tasks. However, they still face significant challenges: they are highly sensitive to…
Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
Yi Ding, Lijun Li, Bing Cao +1
Large Vision-Language Models (VLMs) have achieved remarkable performance across a wide range of tasks. However, their deployment in safety-critical domains poses significant challe…
ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time
Yi Ding, Bolian Li, Ruqi Zhang
Vision Language Models (VLMs) have become essential backbones for multimodal intelligence, yet significant safety challenges limit their real-world application. While textual input…