arXiv:2512.22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation. Existing systems either colocate all stages on a single GPU cluster or decouple them only at a coarse granularity, overlooking hardware heterogeneity and incurring substantial synchronization overhead across stages. We present ROLLART, a system for multi-task agentic RL on disaggregated infrastructure
The increasing complexity and scale of AI models, particularly in agentic reinforcement learning, are pushing the limits of current, monolithic compute infrastructures.
This development addresses critical bottlenecks in training advanced AI agents, which are foundational for future autonomous systems and will enable more efficient scaling of AI capabilities.
The paradigm for training complex AI agents shifts towards disaggregated and specialized infrastructure, moving away from 'one-size-fits-all' GPU clusters.
- · Cloud infrastructure providers
- · Hardware manufacturers (specialized AI accelerators)
- · AI development companies
- · AI researchers
- · Companies with undifferentiated legacy data centers
- · Monolithic AI training software vendors
- · Cloud providers unable to offer disaggregated services
More efficient and faster development of advanced AI agents becomes possible.
This efficiency accelerates the deployment of AI agents into real-world applications, leading to earlier market consolidation.
The optimized use of heterogeneous hardware could lower the financial and energy barriers to developing cutting-edge AI.
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Read at arXiv cs.AI