3 citations · 6 across the 10 of their papers we have counts for
6 papers · 1 filter
Beyond Timestamps: Decision-Aligned On-Policy Distillation for Long-Horizon Agents
Mingju Chen, Can Lv, Jinrong Liu +3
Reinforcement learning with verifiable rewards (RLVR) often relies on sparse outcome rewards, providing coarse supervision for long-horizon agents. On-policy self-distillation (OPS…
Mitigating False Credit Propagation: Probabilistic Graphical Reward Aggregation for Rubric-Based Reinforcement Learning
Can Lv, Mingju Chen, Heng Chang +1
Rubric-based rewards are increasingly used for open-ended language model post-training, but criterion-level scores are often aggregated as independent utilities. This flat scalariz…
Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
Shiji Zhou, Tianbai Yu, Zhi Zhang +4
Machine unlearning (MU) aims to efficiently remove sensitive or harmful memory from a pre-trained model. The key challenge is to balance the potential tradeoff between unlearning e…
On the Limitations and Prospects of Machine Unlearning for Generative AI
Shiji Zhou, Lianzhe Wang, Jiangnan Ye +2
Generative AI (GenAI), which aims to synthesize realistic and diverse data samples from latent variables or other data modalities, has achieved remarkable results in various domain…
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Yongliang Wu, Shiji Zhou, Mingzhuo Yang +6
Text-to-image diffusion models have achieved remarkable success in generating photorealistic images. However, the inclusion of sensitive information during pre-training poses signi…
Robust Multi-Task Learning with Excess Risks
Yifei He, Shiji Zhou, Guojun Zhang +5
Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing met…