2 citations · 2 across the 5 of their papers we have counts for
5 papers
SkyRL-Agent: Efficient RL Training for Multi-turn LLM Agent
Shiyi Cao, Dacheng Li, Fangzhou Zhao +12
We introduce SkyRL-Agent, a framework for efficient, multi-turn, long-horizon agent training and evaluation. It provides efficient asynchronous dispatching, lightweight tool integr…
Empirical Computation: Prompting versus Programming
Eric Tang, Jing Liu, Marcel Böhme
Large Language Models (LLM) can solve *any* computational problem *without* an algorithm in a runtime *independent* of the computational complexity of that problem. Instead of spec…
LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
Dacheng Li, Shiyi Cao, Tyler Griggs +9
Large reasoning models (LRMs) tackle complex reasoning problems by following long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking, and self-validation. Howev…
Streaming Detection of Queried Event Start
Cristobal Eyzaguirre, Eric Tang, Shyamal Buch +3
Robotics, autonomous driving, augmented reality, and many embodied computer vision applications must quickly react to user-defined events unfolding in real time. We address this se…
Understanding LLM Embeddings for Regression
Eric Tang, Bangding Yang, Xingyou Song
With the rise of large language models (LLMs) for flexibly processing information as strings, a natural application is regression, specifically by preprocessing string representati…