5 papers
OPD: Overlap-Gated On-Policy Distillation on Reliable Prefix
Shuaishuai Zu, Jihao Zhao, Pengfei Li +5
On-policy distillation (OPD) supplies dense token-level supervision by aligning a student to the teacher's next-token distribution on its own generated rollouts. Existing works mos…
TraceSpec: Understanding and Improving Speculative Decoding for LLM Agents
Jihao Zhao, Shuaishuai Zu, Pengfei Li +4
LLM agents execute tasks through repeated model calls over evolving histories of reasoning, actions, and tool feedback. Aggregate speculative-decoding metrics merge these distinct…
TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding
Jihao Zhao, Qianyu Jiang, Shuaishuai Zu +6
Parallel drafters such as DFlash and DSpark reduce proposal latency, but early mismatches limit how much of each block can be accepted. Retaining alternatives introduces parent sta…
Compaction-Aware Reinforcement Learning For Ascend Triton Kernel Generation
Chuansai Zhou, Yanlin Liu, Bingning Huang +7
Automatically generating high-performance kernels with LLM-based agents promises substantial gains in development efficiency, but two obstacles remain. First, while increasing mult…
MoE Proxy Models for Low-Cost Failure Reproduction and Diagnosis in LLM RL Post-Training
Yikai Wang, Chuansai Zhou, Yuhang Zhou +10
Reinforcement learning (RL) post-training of large language models (LLMs) is computationally intensive and involves complex system pipelines with substantial debugging overhead. In…