4 citations · 9 across the 21 of their papers we have counts for
7 papers · 1 filter
Exploring the Design Space of Permutation Diffusion LLMs
Zhe Xu, Mingfu Liang, Kavosh Asadi +16
Permutation generation with language models requires both text-conditioned ranking and a global one-to-one assignment between candidates and output positions. We study this problem…
GR2 Technical Report
Yufei Li, Zaiwei Zhang, Mingfu Liang +67
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step dispropo…
Learning to Build the Environment: Self-Evolving Reasoning RL via Verifiable Environment Synthesis
Yucheng Shi, Zhenwen Liang, Kishan Panaganti +3
We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the environments that train it. In z…
DeltaRubric: Generative Multimodal Reward Modeling via Joint Planning and Verification
Rui Liu, Dian Yu, Zhenwen Liang +6
Aligning Multimodal Large Language Models (MLLMs) requires reliable reward models, yet existing single-step evaluators can suffer from lazy judging, exploiting language priors over…
Reinforcing Multimodal Reasoning Against Visual Degradation
Rui Liu, Dian Yu, Haolin Liu +6
Reinforcement Learning has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet the resulting policies remain brittle against real-wor…
SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization
Yan Sun, Guoxia Wang, Jinle Zeng +6
Pretraining large language models (LLMs) with next-token prediction has led to remarkable advances, yet the context-dependent nature of token embeddings in such models results in h…
Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning
Haolin Liu, Dian Yu, Sidi Lu +6
Reinforcement learning (RL) has emerged as a powerful framework for improving the reasoning capabilities of large language models (LLMs). However, most existing RL approaches rely…