2 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
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,…