Xiaoyu Chen
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BORA is an offline-to-online RL framework that enhances dexterous VLA models for real-world robotics by using an action-conditioned critic and a lightweight residual adaptation mechanism to correct execution errors.
The paper introduces SCALR, a novel framework that generates synthetic user-item interaction data from a source domain to augment a target recommendation domain, significantly improving system performance in A/B tests.
Papers
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
Xiangyu Wang, Yawen He, Shivendra Pratap Singh, Han Huang +11 more
The paper introduces SCALR, a novel framework that generates synthetic user-item interaction data from a source domain to augment a target recommendation domain, significantly improving system perform…