24 citations · 114 across the 37 of their papers we have counts for
7 papers · 1 filter
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study
Ruomeng Ding, Qianli Shen, Zhaoyang Han +3
LLM data engineering requires iterative experimentation to determine how data selection, rewards, and curricula affect model learning, making it a natural setting for AI-for-AI (AI…
Attributing Structured-Output Gains in Function Calling: Interface Alignment versus Procedural Transfer
Wanyi Chen, Daoyuan Chen, Fang Kong
Structured-output benchmarks reward both task decisions and interface compliance, so prompt-induced function-calling gains require attribution before they can be interpreted as tra…
Beyond Solvability: Task Learnability as a Static Prior for LLM RL Post-Training
Ting Zhou, Zhenqing Ling, Daoyuan Chen +4
Reinforcement learning (RL) has become a central post-training paradigm for eliciting reasoning capabilities in large language models, yet uniform task sampling allocates compute w…
CDR-Bench: Evaluating Faithful Execution of Compositional, Order-Sensitive Data Refinement Recipes
Yuchen Huang, Xiang Li, Zhenqing Ling +5
Data refinement involves executing multi-step recipes over evolving text states, where both composition and execution order of processing operators determine the outcome. While exi…
GEOALIGN: Geometric Rollout Curation for Robust LLM Reinforcement Learning
Ting Zhou, Zhenqing Ling, Yiyang Zhao +2
Online reinforcement learning is widely used to align large language models (LLMs) with reward signals, yet training can be unstable under noisy or misspecified rewards. We identif…
VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
Zhiwen Li, Zhongjie Duan, Jinyan Ye +4
Replicating In-Context Learning (ICL) in computer vision remains challenging due to task heterogeneity. We propose \textbf{VIRAL}, a framework that elicits visual reasoning from a…
Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
Jinyan Ye, Zhongjie Duan, Zhiwen Li +4
Inference-time scaling offers a versatile paradigm for aligning visual generative models with downstream objectives without parameter updates. However, existing approaches that opt…