Researchers released InterleaveThinker, a reinforcement learning approach for training agents to interleave reasoning tokens with action execution during generation. The method demonstrates improved performance on tasks requiring sequential decision-making by optimizing how agents distribute computational effort between planning and execution steps. Interleaved generation represents a departure from pipeline architectures where reasoning and action are separated. This directly impacts how agent systems allocate tokens—agents can now reserve computational budget dynamically rather than front-loading reasoning. For operators, this changes the cost structure of inference: tasks may execute faster through early action commits, or slower if agents require distributed reasoning. The approach also implies that token efficiency metrics need revision; total token count becomes less predictive than token distribution patterns. For builders, this signals a need to rethink RL reward structures for agents. Training pipelines must now support mixed reasoning-action sequences rather than discrete phases. Existing agent frameworks optimized around sequential planning followed by execution may require architectural refactoring to support interleaved generation without efficiency penalties.