26 citations · 28 across the 5 of their papers we have counts for
5 papers
Rethinking the Design of Reinforcement Learning-Based Deep Research Agents
Yi Wan, Jiuqi Wang, Liam Li +3
Large language models (LLMs) augmented with external tools are increasingly deployed as deep research agents that gather, reason over, and synthesize web information to answer comp…
An Empirical Study of Deep Reinforcement Learning in Continuing Tasks
Yi Wan, Dmytro Korenkevych, Zheqing Zhu
In reinforcement learning (RL), continuing tasks refer to tasks where the agent-environment interaction is ongoing and can not be broken down into episodes. These tasks are suitabl…
Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model
Yu Cui, Feng Liu, Pengbo Wang +5
Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the…
Pearl: A Production-ready Reinforcement Learning Agent
Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari +12
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performan…
Time-Dependent Blackwell Approachability and Application to Absorbing Games
Joon Kwon, Yijun Wan, Bruno Ziliotto
Blackwell's approachability (Blackwell, 1954, 1956) is a very general online learning framework where a Decision Maker obtains vector-valued outcomes, and aims at the convergence o…