Andy Peng — accepted AI-conference papers

2026

Self-Improving Vision-Language-Action Models with Data Generation via Residual RL

Wenli Xiao, Haotian Lin, Andy Peng, Haoru Xue, Tairan He, Zhengyi Luo +5

ICLR 2026poster

Supervised fine-tuning (SFT) has become the de facto post-training strategy for large vision-language-action (VLA) models, but its reliance on costly human demonstrations limits scalability and generalization. We propose Probe, Learn, Distill (PLD), a plug-and-play framework that improves VLAs throu…

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2025

Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data

Zhiyuan Zhou, Andy Peng, Qiyang Li, Sergey Levine, Aviral Kumar

ICLR 2025poster

The modern paradigm in machine learning involves pre-training on diverse data, followed by task-specific fine-tuning. In reinforcement learning (RL), this translates to learning via offline RL on a diverse historical dataset, followed by rapid online RL fine-tuning using interaction data. Most RL fi…

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