Long agent rollouts turn reinforcement learning into a systems problem
Training agents with reinforcement learning now depends on keeping the model, inference engine, harness, and sandbox consistent, or the update can learn from differences unrelated to the model. The post explains how long tool-using sessions amplify those mismatches, with QwenGyre rollouts averaging about two hours for a 122B model and often exceeding four hours for a 2.4T model. Alibaba routes requests through a proxy, while DeepSeek freezes sandbox state when jobs pause.