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Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training

Yuming Zhou, Haoyang Li, Sheng Lin, Yanfeng Zhao 2026-06-14
Language Model × LLM Training GPU Hardware

ForeMoE addresses expert load imbalance in Mixture-of-Experts (MoE) models during reinforcement learning (RL) post-training, where existing step-level statistics fail due to high-frequency micro-step fluctuations. The method exploits foreseeable routing information from the rollout stage to proactively guide load balancing, using a hierarchical planner to decompose the NP-hard problem and a transfer engine for overlapped expert transfer. Evaluations on 64 GPUs show up to a 1.45× speedup over state-of-the-art RL post-training systems. This matters because it enables efficient scaling of MoE LLMs under the unique workload dynamics of RL post-training, a dominant paradigm in current LLM development.

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An LLM System for Autonomous Variational Quantum Circuit Design

Kenya Sakka, Wataru Mizukami, Kosuke Mitarai 2026-06-14
Language Model × LLM Machine Learning × Hardware Quant Ph

The problem is that designing high-performing quantum circuits remains heavily reliant on human expertise. The method introduces an autonomous agentic framework using LLMs with seven integrated components for iterative circuit design under explicit constraints. Experimental evidence shows the framework outperforms representative quantum feature maps on image classification and achieves competitive accuracy for molecular ground state estimation across seven molecules. This matters because it establishes LLM-driven agentic systems as a viable paradigm for automated quantum circuit design and demonstrates AI's role in iterative scientific optimization.

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nomp: A Framework for Building Domain Specific Compilers

Thilina Ratnayaka, Kaushik Kulkarni, Nipuna Fernando, Pubudu Hewavitharana 2026-06-14
OpenMP CUDA HPC Compiler Runtime

Problem: Existing GPU programming models force a trade-off between low-level performance and high-level productivity, with no single solution achieving all three goals of productivity, portability, and performance. Method: The authors propose nomp, a framework for building domain-specific compilers that uses a pragma-based programming model and a runtime for code transformation and generation based on user-provided metadata. Finding or experimental evidence: The abstract does not disclose experimental results. Why it matters: nomp aims to improve programmer productivity without sacrificing performance or portability by enabling reuse of domain-specific optimization patterns.

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