2 papers
cs.AI2026
RMSWeb: Reflection, Failure-Mode Mining, and Salvage-DS for Web Agent Reinforcement Learning
Chengbo Liu, Lifang Zhou, Ruijie Yan +8
Compact web agents can reduce deployment cost, but training them poses challenges in both data collection and post-SFT reinforcement learning (RL). Successful trajectories are expe…
cs.CL2024
SDSAT: Accelerating LLM Inference through Speculative Decoding with Semantic Adaptive Tokens
Chengbo Liu, Yong Zhu
We propose an acceleration scheme for large language models (LLMs) through Speculative Decoding with Semantic Adaptive Tokens (SDSAT). The primary objective of this design is to en…