Multi-Turn RL: A Guide to Getting Reinforcement Learning Right

Multi-turn reinforcement learning for agents is presented as primarily an inference-scaling challenge rather than a gradient-optimization problem, because long, variable-length trajectories require repeated model calls and replay increasingly large conversation contexts. The discussion explains that rollout costs grow roughly quadratically with the number of turns due to expanding prompts, while synchronous training batches can leave GPUs underutilized as they wait for slow rollouts. It recommends decoupling rollout generation from policy training, using asynchronous worker pools and hosted APIs for fixed-weight tasks such as environment simulation, reward judging, baseline evaluation, and tool responses, while reserving local infrastructure for the continually changing open-weight policy. It also advocates choosing different models for policy, simulation, judging, and performance baselines based on their distinct cost and capability requirements, applying trajectory-level rewards with explicit penalties for failures such as malformed tool calls or turn-limit exhaustion, and separately tracking reward components to identify reward hacking or judge failures. Cost examples illustrate that judge inputs and simulator outputs can dominate spending, while prefix caching can substantially reduce repeated-context costs. Finally, it advises teams to establish independent evaluations and verify that a base policy has meaningful initial success—suggested at roughly 20%—before committing to expensive RL training, since stronger models, improved scaffolding, or agent frameworks may offer cheaper gains.

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