RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning
arXiv:2606.26997v1 Announce Type: cross Abstract: Large language model (LLM) post-training for reasoning increasingly relies on reinforcement learning with verifiable rewards (RLVR), where models learn from ground-truth feedback on mathematical, logical, and scientific tasks. To enable flexible resource allocation and support heterogeneous training setups, modern RLVR systems adopt disaggregated architectures that decouple rollout generation and policy training across independent GPU pools. However, existing synchronous on-policy GRPO (Group Relative Policy Optimization) RLVR systems finish an
This research addresses current bottlenecks in large language model (LLM) training efficiency, a critical area given the rapid advancement and increasing scale of AI development.
Improved reinforcement learning techniques for LLMs are vital for developing more capable AI, particularly for complex reasoning tasks, which will impact various industries and strategic capabilities.
The proposed 'RolloutPipe' system offers a more efficient method for training disaggregated on-policy LLM reinforcement learning models, potentially accelerating the development cycle for advanced AI.
- · AI developers
- · Cloud computing providers
- · SaaS companies leveraging LLMs
- · Companies with inefficient AI training infrastructure
- · Organisations reliant on older RL methods
Faster and more cost-effective development of sophisticated LLMs for reasoning tasks.
Accelerated adoption of AI agents in complex decision-making and automation roles.
Enhanced AI capabilities contributing to a broader AI race among nations and corporations.
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Read at arXiv cs.LG