PLANTAIN: Plan-Answer Interleaved Reasoning

Reasoning models often spend a lot of time thinking before they generate a visible response. This creates a frustrating, but unfortunately common, experience: the user's time is wasted while the model reasons from a false premise that could have easily been corrected. In contrast, human speakers perform lightweight, incremental check-ins to ensure that conversational participants stay on common ground. With this motivation, we propose \textit{interleaved reasoning} (IR), in which the model alternates between thinking and surfacing intermediate responses, as an alternative to the standard ``think-then-answer'' approach. By providing useful information to the user earlier, IR reduces perceived latency, the time a user waits for an initial output, without compromising the quality of the final response. We focus on a specialization of interleaved reasoning, \method (\textul{Plan}-\textul{T}hought-\textul{A}nswer \textul{In}terleaving), where the first intermediate response is an explicit, step-by-step \textit{plan} for executing the task. This plan-first strategy allows for user intervention and early feedback for subsequent reasoning steps. \method\ yields an $\sim$6\% improvement in pass@1 across several challenging math reasoning and coding benchmarks, while reducing time-to-first-response by over 60\% relative to think-then-answer baselines.