reinforcement learning

ReLA: Representation Learning and Aggregation for Scalable Job Scheduling with Reinforcement Learning

Large-scale job scheduling is a classic problem in computing systems and industrial operations, where complex workloads, workflows, or ordered job operations must be assigned to computing nodes or machines under resource, precedence, and availability constraints. Existing solvers can provide useful reference solutions,...

MudGym

A reinforcement learning environment for MUD2.

SCPFL: A Safety Reinforcement Learning Framework for Joint Energy-Fairness Optimization in Long-term Federated Learning

Federated Learning (FL) enables heterogeneous clients to collaboratively train a shared model while keeping raw data local. In practical FL systems, client participation is often constrained by limited battery energy, and long-term unselection of clients may lower their willingness to participate, which further reduces...

Accelerating Long-Tail Generation in Synchronous RLHF Training via Adaptive Tensor Parallelism

Reinforcement Learning from Human Feedback (RLHF) has become a key post-training paradigm for improving model quality. However, the synchronous three-stage RLHF pipeline is often bottlenecked by the generation stage, where response-length skew causes the effective batch size to shrink rapidly during decoding, leaving G...

Large Language Model-Enhanced Multi-Agent Reinforcement Learning for Autonomous Enterprise Workflow Orchestration via Graph Semantic Routing

# LeMARLA **Large Language Model-Enhanced Multi-Agent Reinforcement Learning for Autonomous Enterprise Workflow Orchestration via Graph Semantic Routing.** LeMARLA couples an LLM-driven task decomposition module, a heterogeneous graph attention routing module over tasks, employees and skills, and a CTDE multi-agent rei...

Multilingual Sentiment-Aware Text Summarization: A Reinforcement Learning Approach for Consistency Maintenance

LLM Reasoning for Subjective Tasks: Failure Modes, Mitigation, and Dynamic Reasoning Routing

This work introduces a conditional length-penalized post-training algorithm that intertwines verification accuracy with bounded reasoning length, halting collapse and recovering performance, and offers both a scalable algorithmic patch and a long-term architectural blueprint for aligning reasoning models with real-worl...

Reinforcement learning-based fault-tolerant formation control for multi-hypersonic flight vehicles with prescribed performance shaping

Event-triggered reinforcement learning-based fractional-order sliding mode control with neural approximation for floating offshore wind turbines

WaveGRL: A wavelet-graph reinforcement learning framework for sequential imputation of ocean time series

A safety framework for autonomous underwater vehicle navigation based on safety-constrained reinforcement learning

Two-stage reinforcement learning-guided memetic algorithm for solving energy-saving distributed flexible job shop scheduling with machine restart-aware