5 papers
The Model Knows Another Way: Strategy Switching for Effective RLVR Exploration
Jin Cui, Xinyue Long, Boran Zhao +2
Reinforcement learning with verifiable rewards (RLVR) is often limited by insufficient exploration: difficult problems can yield uniformly incorrect rollout groups and therefore li…
IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning
Yihao Qin, Yuanfei Wang, Hang Zhou +3
Decision transformer based sequential policies have emerged as a powerful paradigm in offline reinforcement learning (RL), yet their efficacy remains constrained by the quality of…
InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy
Yang Tian, Yuyin Yang, Yiman Xie +13
Recent works explore how real and synthetic data contribute to Vision-Language-Action (VLA) models' generalization. While current VLA models have shown the strong effectiveness of…
Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
Yang Tian, Sizhe Yang, Jia Zeng +4
Current efforts to learn scalable policies in robotic manipulation primarily fall into two categories: one focuses on "action," which involves behavior cloning from extensive colle…
Learning Manipulation by Predicting Interaction
Jia Zeng, Qingwen Bu, Bangjun Wang +12
Representation learning approaches for robotic manipulation have boomed in recent years. Due to the scarcity of in-domain robot data, prevailing methodologies tend to leverage larg…