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ResearchOctober 12, 2026

Qwen introduced QwenGyre: training AI agents has become up to 1.85 times faster

🚀 Qwen introduced QwenGyre: training AI agents up to 1.85 times faster Modern agents can perform tasks for hours, generating hundreds of thousands of tokens. When training such systems, GPUs are often idle, and repeated trajectories waste computing resources. QwenGyre…

Machinelearning·Telegram — AI Machine Learning Big Data
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Key Takeaways
  • 01.🚀 Qwen introduced QwenGyre: training AI agents up to 1.85 times faster Modern agents can perform tasks for hours, generating hundreds of thousands of tokens
  • 02.When training such systems, GPUs are often idle, and repeated trajectories waste computing resources
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Qwen introduced QwenGyre: training AI agents has become up to 1.85 times faster

🚀 Qwen представила QwenGyre: обучение ИИ-агентов стало до 1,85 раза быстрее Современные агенты могут выполнять задачи часами, генерируя сотни тысяч токенов. При обучении таких систем GPU часто простаивают, а повторяющиеся траектории расходуют вычислительные ресурсы. QwenGyre решает обе проблемы: • Elastic Scheduler динамически распределяет GPU между генерацией траекторий и обучением, не прерывая активные задачи. • Trajectory Processor объединяет разветвлённые траектории, удаляет дубликаты и учитывает даже частично выполненные задачи. Результаты впечатляют: → До 1,85× ускорения относительно Colocate. → До 1,78× относительно Async. → До 700 000 токенов на одну траекторию. → Рост точности на NL2RepoBench с 52,5% до 58,5% всего за 48 шагов обучения. 📄 Статья: https://arxiv.org/abs/2609.33848

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