107 citations · 142 across the 21 of their papers we have counts for
7 papers · 1 filter
BAGEN: Are LLM Agents Budget-Aware?
Yuxiang Lin, Zihan Wang, Mengyang Liu +9
While agents are increasingly spending more resources, today agent cost is mostly measured only after execution. A Budget-Aware Agent (BAGEN) should treat budget as an active contr…
When does Chain-of-Thought Help: A Markovian Perspective
Zihan Wang, Yijun Dong, Qi Lei
Chain-of-Thought (CoT) prompting is a widely used inference-time technique for improving reasoning, yet its gains are uneven across tasks. We analyze when and why CoT helps by mode…
Artificial Entanglement in the Fine-Tuning of Large Language Models
Min Chen, Zihan Wang, Canyu Chen +3
Large language models (LLMs) can be adapted to new tasks using parameter-efficient fine-tuning (PEFT) methods that modify only a small number of trainable parameters, often through…
A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning
Licheng Liu, Zihan Wang, Linjie Li +5
Multi-turn problem solving is critical yet challenging for Large Reasoning Models (LRMs) to reflect on their reasoning and revise from feedback. Existing Reinforcement Learning (RL…
Weak-to-Strong Generalization with Failure Trajectories: A Tree-based Approach to Elicit Optimal Policy in Strong Models
Ruimeng Ye, Zihan Wang, Yang Xiao +3
Weak-to-Strong generalization (W2SG) is a new trend to elicit the full capabilities of a strong model with supervision from a weak model. While existing W2SG studies focus on simpl…
Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models
Zihan Wang, Rui Pan, Jiarui Yao +7
We propose Chain-of-Experts (CoE), a new Mixture-of-Experts (MoE) architecture that introduces sequential expert communication within each layer. Unlike traditional MoE models, whe…
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Zihan Wang, Kangrui Wang, Qineng Wang +15
Training large language models (LLMs) as interactive agents presents unique challenges including long-horizon decision making and interacting with stochastic environment feedback.…