6 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing
Xiaolong Xu, Hao Jiang, Bo Jiang +5
Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for sequence synthesis rather than…
OptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization
Chenxing Wei, Sichen Liu, Lizhao Liu +6
Large language models (LLMs) are increasingly deployed to solve complex scientific and practical problems via iterative optimization. However, dynamically coordinating diverse sear…
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
Chenyu Huang, Jianghao Lin, Zhengyang Tang +4
We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated by recent advances in LLM-based e…
Efficient Token-Level Policy Optimization for Masked Diffusion Models via Velocity Alignment
Chenxing Wei, Jiazhen Kang, Hong Wang +8
Reinforcement Learning with Verifiable Rewards (RLVR) successfully aligns autoregressive models in rigorous domains like mathematical reasoning and code generation. However, applyi…
Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
Chenxing Wei, Hong Wang, Ying He +4
Test-time policy adaptation for multi-turn interactions (T2PAM) is essential for aligning Large Language Models (LLMs) with dynamic user needs during inference time. However, exist…