9 citations · 15 across the 7 of their papers we have counts for
7 papers
Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents
Zechuan Wang, Siyuan Lu, Hongxuan Zhang +3
Reinforcement learning with verifiable rewards (RLVR) directly optimizes verifier-defined outcomes for multi-turn tool-use agents, yet its trajectory-level credit assignment confla…
Don't Just Fine-tune the Agent, Tune the Environment
Siyuan Lu, Zechuan Wang, Hongxuan Zhang +5
Large Language Model (LLM) agents show great promise for complex, multi-turn tool-use tasks, but their development is often hampered by the extreme scarcity of high-quality trainin…
RAG-R1: Incentivizing the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
Zhiwen Tan, Jiaming Huang, Qintong Wu +3
Large Language Models (LLMs), despite their remarkable capabilities, are prone to generating hallucinated or outdated content due to their static internal knowledge. While Retrieva…
CSR:Achieving 1 Bit Key-Value Cache via Sparse Representation
Hongxuan Zhang, Yao Zhao, Jiaqi Zheng +3
The emergence of long-context text applications utilizing large language models (LLMs) has presented significant scalability challenges, particularly in memory footprint. The linea…
GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System
Xingyu Lu, Zhining Liu, Yanchu Guan +6
Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as new…
On the Opportunities of Green Computing: A Survey
You Zhou, Xiujing Lin, Xiang Zhang +38
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including…
Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster
Hongxuan Zhang, Zhining Liu, Yao Zhao +4
In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself. FastCo…