MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service
Reinforcement Learning from Verifiable Rewards (RLVR) has significantly improved the reasoning capabilities of large language models (LLMs), particularly in multi-turn agentic settings involving environment interaction like tool use. However, fine-tuning such models remains prohibitively expensive due to high computational requirements, limiting accessibility. We propose MARLaaS (Multi-tenant Asynchronous RL as a Service), a system for concurrent RL fine-tuning across multiple users and tasks. Our approach is based on two key ideas: (1) sharing a base model across tenants using lightweight LoRA adapters, and (2) a disaggregated asynchronous architecture that decouples rollout generation, ...
| Authors | Timothy Tin Long Yu, Gursimran Singh, Ge Shi et al. |
|---|---|
| Primary category | cs.DC |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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