Reinforcement learning is an infrastructure problem

Modal argues that infrastructure has become the principal bottleneck in reinforcement-learning post-training for large language models, as training requires coordinated multi-node model optimization, high-throughput inference rollouts, and vast numbers of isolated execution environments. It emphasizes that larger open-weight models offer stronger capabilities but make weight synchronization costly, particularly across nodes, while RDMA networking, LoRA, asynchronous training, and delta compression can substantially reduce transfer delays and idle GPU expenses. The company identifies recurring operational challenges including extensive integration code, limited cluster availability, and GPU underuse caused by slow or insufficiently scaled sandbox environments. Modal presents its platform as an abstraction layer for RDMA-connected GPU clusters, fault tolerance, autoscaling, and large-scale sandboxes, allowing teams to focus on rewards, environments, and algorithms rather than infrastructure. It also supports open-source training frameworks such as slime, verl, and OpenRLHF, contributes improvements upstream, and has introduced the experimental open-source Modal Training Gym to simplify defining RL jobs around a model, reward function, and environment with built-in observability and tutorials.

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