GPlaceRL: An Open-Source Graph Reinforcement Learning Framework for Detailed Placement

Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement

Improving chip macro placement via GFlowNet-guided tree search

This study addresses the long-standing challenge of a core task in chip physical design, termed macro placement. The problem is characterized by an enormous combinatorial search space and a highly multimodal cost landscape. While recent learning-based approaches have shown promise, reinforcement-learning rollouts often...

From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs

The integration of graphs with Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) has received increasing attention, as graphs naturally encode task hierarchies for effective subgoal sampling. However, existing methods often overlook intrinsic connectivity information, failing to fully leverage the underlying...

A Center-Guided Reinforcement Learning Method for Hyperparameter Optimization and Its Application to Relation Extraction

The effectiveness of CGRL for limited-budget HPO and its applicability to practical RE tasks are demonstrated and a process-aware reward provides dense and informative feedback for policy learning.

Expert-Space Exploration in MoE Reinforcement Learning

Reinforcement learning (RL) has become central to post-training of large language models. Recent advances in RL for Mixture-of-Experts (MoE) models have primarily focused on improving optimization stability and training efficiency, while treating the expert selection as a fixed component. Since routing determines the s...

Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing

The one-dimensional bin packing problem (1D-BPP) is a classical NP-hard combinatorial optimization problem with applications ranging from logistics and manufacturing to cloud resource management. Although deep reinforcement learning (DRL) has become a competitive paradigm for data-driven optimization, most learned pack...