1 citations · 1 across the 16 of their papers we have counts for
5 papers · 1 filter
ValuSpec: Plug-and-Play Candidate Valuation before Target Verification for Tree-Based Speculative Decoding
Liang He, SiYuan Ma, Qishi Zhan +5
Tree-based speculative decoding accelerates large language model inference by verifying multiple candidate branches in parallel. However, in practical serving scenarios, similar re…
Let Credit Follow Computation: Architecture-Aware Credit Transport for Large Language Model Reinforcement Learning
Qifan Shi, Zhaolu Kang, Chenghua Zhu
Credit assignment in large-language-model reinforcement learning (LLM RL) can be separated into three objects: evidence about success, a transport operator that converts this evide…
MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations
Qiming Shi, Yulong Tao, Linbo Jin +10
Large language model agents are increasingly evaluated as autonomous tool users, yet most benchmarks focus on bounded tasks with immediate success criteria. Real-world deployments…
SKILL-KD: Contrastive Skill Distillation for LLM Agents
Qiming Shi, Yibo Dou, Jiawen Zhu +5
Skill-based prompting has become a practical mechanism for improving LLM agents, yet existing methods often treat skills as summaries of the agent's own experience or of successful…
FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models
Kehan Jiang, Haonan Dong, Zhaolu Kang +2
Recent Large Reasoning Models (LRMs) like DeepSeek-R1 have demonstrated remarkable success in complex reasoning tasks, exhibiting human-like patterns in exploring multiple alternat…