16 papers
Sketched Linear Contrastive Learning: Approximation, Optimization, and Statistical Scaling
Ziyan Chen, Zhongzhu Zhou, Ding-Xuan Zhou
Scaling laws describe how learning performance varies with model size, data size, and compute. While recent theoretical work has established scaling laws for sketched linear regres…
Scaling Laws for Dynamic Mini-Batch SGD in Sketched Linear Regression
Ziyan Chen, Zhongzhu Zhou, Ding-Xuan Zhou
Mini-batching is central to large-scale optimization, yet its role in statistical scaling laws remains limited. We study one-pass and multi-pass batch SGD for sketched linear regre…
Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach
Chanwoo Park, Ziyang Chen, Asuman Ozdaglar +1
Large language models (LLMs) are increasingly deployed as "agents" for decision-making (DM) in interactive and dynamic environments. Yet, since they were not originally designed fo…
Can Broad Biomedical Knowledge be Contextualized into Scenario-Grounded Propositions?
Qingyuan Zeng, Ziyang Chen, Pengxiang Cai +5
Biomedical discovery often requires connecting broad biomedical knowledge with specific experimental or clinical data. Background knowledge suggests relevant mechanisms but is usua…
Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models
Qingyuan Zeng, Pengxiang Cai, Zixin Guan +5
Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard single-target generation beca…
GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment
Minxuan Lv, Tiehua Mei, Tanlong Du +9
We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR). Existing long-context RL…