5 papers
CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification
Jinpeng Chen, Cheng Gong, Hanbo Li +9
Developing multi-turn interactive tool-use agents is challenging because real-world user needs are often complex and ambiguous, yet agents must execute deterministic actions to sat…
Evaluating GFlowNet from partial episodes for stable and flexible policy-based training
Puhua Niu, Shili Wu, Xiaoning Qian
Generative Flow Networks (GFlowNets) were developed to learn policies for efficiently sampling combinatorial candidates by interpreting their generative processes as trajectories i…
Multi-agent Robust and Optimal Policy Learning for Data Harvesting
Shili Wu, Yancheng Zhu, Aniruddha Datta +1
We consider the problem of using multiple agents to harvest data from a collection of sensor nodes (targets) scattered across a two-dimensional environment. These targets transmit…
Robust Behavior Cloning Via Global Lipschitz Regularization
Shili Wu, Yizhao Jin, Puhua Niu +2
Behavior Cloning (BC) is an effective imitation learning technique and has even been adopted in some safety-critical domains such as autonomous vehicles. BC trains a policy to mimi…
GFlowNet Training by Policy Gradients
Puhua Niu, Shili Wu, Mingzhou Fan +1
Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties. We here propose a new GFlowNet training framework, with po…