这篇论文提出了PACT，用一个小型语言模型来规划动作，再结合强化学习策略，在FrozenLake上比纯RL效果好，而且不用重训练。
PACT是一种混合架构，结合快速反应式RL策略与慢速SLM规划器。它异步调用2B参数SLM生成候选动作计划，经仿真验证安全、可行、完整后直接执行，无需重训练RL策略。在三个难度递增的FrozenLake配置上，PACT超越所有基线，表明规划与反应协同比单独使用更强。
When in Doubt, Plan It Out: Committed Small Language Model Deliberation for Reactive Reinforcement Learning
Reinforcement Learning (RL) policies often degrade in unfamiliar environments because they lack explicit deliberation. We propose Plan, Align, Commit, Think (PACT), a hybrid architecture that combines a fast, reactive RL policy with a slow, deliberative Small Language Model (SLM) planner. PACT invokes the SLM asynchronously to generate and validate candidate action plans. Once a plan is verified through simulation as safe, feasible, and complete, it is executed directly, bypassing the RL policy without retraining or modifying it. Evaluated on three FrozenLake configurations of increasing difficulty, PACT outperforms all baselines while relying on a 2B-parameter SLM backbone, suggesting that deliberative planning and reactive execution are more powerful in concert than either is alone in these settings.