3 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Distilled Reinforcement Learning for LLM Post-training
Chen Wang, Zhaochun Li, Jionghao Bai +4
Large language model (LLM) post-training is essential for improving reasoning, adaptation, and alignment. Existing methods mainly follow two paradigms: reinforcement learning (RL)…
HTAM: Hierarchical Transition-Attended Memory for Operator Optimization
Yining Zhang, Mingyang Yi, Chen Wang +5
High-performance GPU kernels are essential for efficient LLM deployment, yet optimizing them remains expertise-intensive. Recent LLM-based code generation makes automatic GPU opera…
Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training
Chen Wang, Hexuan Deng, Yining Zhang +5
Reinforcement learning with verifiable rewards improves LLM reasoning but often induces overthinking, where models generate unnecessarily long reasoning traces. Existing methods ma…
TENSURE: Fuzzing Sparse Tensor Compilers (Registered Report)
Kabilan Mahathevan, Yining Zhang, Muhammad Ali Gulzar +1
Sparse Tensor Compilers (STCs) have emerged as critical infrastructure for optimizing high-dimensional data analytics and machine learning workloads. The STCs must synthesize compl…