7 papers
LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget
Changhai Zhou, Kieran Liu, Yuhua Zhou +17
LongStraw introduces an execution framework that enables reinforcement‑learning post‑training on million‑token prompts using a fixed GPU budget by separating prompt evaluation from…
BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference
Yuhua Zhou, Shaoqi Yu, Shichao Weng +4
Large language models (LLMs) incur high inference cost due to their depth and parameter scale. Depth pruning can reduce latency by skipping redundant Transformer blocks, but existi…
MinT: Managed Infrastructure for Training and Serving Millions of LLMs
Mind Lab, :, Song Cao +60
We present MindLab Toolkit (MinT), a managed infrastructure system for Low-Rank Adaptation (LoRA) post-training and online serving. MinT targets a setting where many trained polici…
CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction
Jun Gao, Yun Peng, Qian Qiao +6
Despite strong performance on code generation tasks, it remains unclear whether large language models (LLMs) genuinely reason about code execution. Existing code reasoning benchmar…
Large Language Model Compression with Global Rank and Sparsity Optimization
Changhai Zhou, Qian Qiao, Yuhua Zhou +4
Low-rank and sparse composite approximation is a natural idea to compress Large Language Models (LLMs). However, such an idea faces two primary challenges that adversely affect the…
AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning
Changhai Zhou, Shiyang Zhang, Yuhua Zhou +5
Quantization followed by parameter-efficient fine-tuning has emerged as a promising paradigm for downstream adaptation under tight GPU memory constraints. However, this sequential…