5 citations · 6 across the 13 of their papers we have counts for
4 papers · 1 filter
Transformers are Efficient Compilers, Provably
Xiyu Zhai, Runlong Zhou, Liao Zhang +1
Transformer-based large language models (LLMs) have demonstrated surprisingly robust performance across a wide range of language-related tasks, including programming language under…
The Crucial Role of Samplers in Online Direct Preference Optimization
Ruizhe Shi, Runlong Zhou, Simon S. Du
Direct Preference Optimization (DPO) has emerged as a stable, scalable, and efficient solution for language model alignment. Despite its empirical success, the optimization propert…
Rethinking Transformers in Solving POMDPs
Chenhao Lu, Ruizhe Shi, Yuyao Liu +3
Sequential decision-making algorithms such as reinforcement learning (RL) in real-world scenarios inevitably face environments with partial observability. This paper scrutinizes th…
Reflect-RL: Two-Player Online RL Fine-Tuning for LMs
Runlong Zhou, Simon S. Du, Beibin Li
As language models (LMs) demonstrate their capabilities in various fields, their application to tasks requiring multi-round interactions has become increasingly popular. These task…