Runpeng Dai

I am a final-year Ph.D. student at the University of North Carolina at Chapel Hill, advised by Prof. Hongtu Zhu. My research lies at the intersection of Reinforcement Learning and LLM Reasoning, with a focus on addressing real-world challenges in areas such as efficient inference and information retrieval.

Seeking full-time opportunities as an AI Researcher or AI/ML Engineer in the Bay Area or North Carolina.

It Takes Two to Match: Co-Evolving Generative Retriever with Reinforcement Learning

Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling

LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling

CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing

R1-RE: Cross-Domain Relation Extraction with RLVR

Parallel-R1: Towards Parallel Thinking via Reinforcement Learning

Breach in the Shield: Unveiling the Vulnerabilities of Large Language Models

Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences

Deep Distributional Learning with Non-crossing Quantile Network

Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing