7 citations · 17 across the 14 of their papers we have counts for
15 papers
Process Reward Informed Tree Rollout for Effective Multi-Turn RL
Xintong Li, Sha Li, Yuwei Zhang +8
Reinforcement learning (RL) has become a key approach for training LLM agents, yet popular methods such as GRPO/RLOO rely on multiple independently sampled complete trajectories fo…
HERO: Hindsight-Enhanced Reflection from Environment Observations for Agentic Self-Distillation
Haoran Liu, Yuwei Zhang, Xiyao Li +2
Reinforcement learning typically improves multi-turn agent capabilities through the terminal outcome of the trajectories, which makes it difficult to determine credit assignments f…
CoMem: Context Management with A Decoupled Long-Context Model
Yuwei Zhang, Chengyu Dong, Shuowei Jin +11
Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this process typically incurs subs…
ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
Zhongkai Yu, Yichen Lin, Chenyang Zhou +12
Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, re…
Learning with Rare Success but Rich Feedback via Reflection-Enhanced Self-Distillation
Yuwei Zhang, Sha Li, Changlong Yu +9
Enabling Large Language Models (LLMs) to continuously improve from environmental interactions is a central challenge in post-training. While on-policy self-distillation offers a pr…
RISE: Rule-Driven SQL Dialect Translation via Query Reduction
Xudong Xie, Yuwei Zhang, Wensheng Dou +5
Translating SQL dialects across different relational database management systems (RDBMSs) is crucial for migrating RDBMS-based applications to the cloud. Traditional SQL dialect tr…
MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Guangchen Lan, Sipeng Zhang, Tianle Wang +7
As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with human preferences and improving perfor…
Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection
Yuwei Zhang, Wenhao Yu, Shangbin Feng +5
Despite significant advances in large language models (LLMs), their knowledge memorization capabilities remain underexplored, due to the lack of standardized and high-quality test…
Attention Reveals More Than Tokens: Training-Free Long-Context Reasoning with Attention-guided Retrieval
Yuwei Zhang, Jayanth Srinivasa, Gaowen Liu +1
Large Language Models (LLMs) often exhibit substantially shorter effective context lengths than their claimed capacities, especially when handling complex reasoning tasks that requ…
Toward Multi-Session Personalized Conversation: A Large-Scale Dataset and Hierarchical Tree Framework for Implicit Reasoning
Xintong Li, Jalend Bantupalli, Ria Dharmani +2
There has been a surge in the use of large language models (LLM) conversational agents to generate responses based on long-term history from multiple sessions. However, existing lo…
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
Di Wu, Hongwei Wang, Wenhao Yu +3
Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized re…
Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting
Zilong Wang, Zifeng Wang, Long Le +9
Retrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date respo…
Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals
Qingyang Wu, Ying Xu, Tingsong Xiao +8
Large Language Models (LLMs) have emerged as potent tools for advancing the United Nations' Sustainable Development Goals (SDGs). However, the attitudinal disparities between LLMs…
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders
Yuwei Zhang, Siffi Singh, Sailik Sengupta +4
Conversational systems often rely on embedding models for intent classification and intent clustering tasks. The advent of Large Language Models (LLMs), which enable instructional…
Answer is All You Need: Instruction-following Text Embedding via Answering the Question
Letian Peng, Yuwei Zhang, Zilong Wang +4
This work aims to build a text embedder that can capture characteristics of texts specified by user instructions. Despite its tremendous potential to deploy user-oriented embedding…