5 papers
DSB: Dynamic Sliding Block Scheduling for Diffusion LLMs
Lizhuo Luo, Shenggui Li, Yonggang Wen +1
Diffusion large language models (dLLMs) have emerged as a promising alternative for text generation, distinguished by their native support for parallel decoding. In practice, block…
SpecForge: A Flexible and Efficient Open-Source Training Framework for Speculative Decoding
Shenggui Li, Chao Wang, Yikai Zhu +14
Large language models incur high inference latency due to sequential autoregressive decoding. Speculative decoding alleviates this bottleneck by using a lightweight draft model to…
CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
Qiaoling Chen, Zhisheng Ye, Tian Tang +7
Batch inference for agentic workloads stresses the GPU key-value (KV) cache in a sustained and cumulative manner, often causing severe throughput degradation well before memory cap…
ReSpec: Towards Optimizing Speculative Decoding in Reinforcement Learning Systems
Qiaoling Chen, Zijun Liu, Peng Sun +6
Adapting large language models (LLMs) via reinforcement learning (RL) is often bottlenecked by the generation stage, which can consume over 75\% of the training time. Speculative d…
SPPO:Efficient Long-sequence LLM Training via Adaptive Sequence Pipeline Parallel Offloading
Qiaoling Chen, Shenggui Li, Wei Gao +3
In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities, driving advancements in real-world applications. However, training LLMs on increasingly long i…