QwenGyre: Training AI Agents 1.85× Faster on Long-Horizon Tasks

Training AI agents on long, complex tasks can be slow and expensive. QwenGyre tackles this with elastic reinforcement learning (RL), dynamically reallocating GPUs between agent execution and model training without pausing ongoing tasks. Its trajectory processor also organizes branching agent workflows into trees, reuses shared steps, and preserves progress from timed-out tasks. The results? Up to 1.85× faster training than Colocate, and a 44% reduction in total training time compared with Async. On NL2RepoBench, scores improved from 52.5% to 58.5% in just 48 steps. Read the research paper: https://arxiv.org/abs/2609.33848 Follow Analytics Vidhya for more AI research and updates. #QwenGyre #AgenticAI #ReinforcementLearning #AIResearch #LLMAgents #AlibabaQwen #MachineLearning #AnalyticsVidhya