Shorten After You’re Right: Lazy Length Penalties for Reasoning RL

Long-reasoning models achieve strong accuracy on complex reasoning tasks, but their extended reasoning trajectories incur substantial memory and latency costs.Several existing shortening methods rely on additional supervision or multi-stage post-training, which primarily reduces inference length and does not reduce the rollout tokens during on-policy reinforcement learning (RL).We instead target onpolicy response shortening, aiming to improve both inference efficiency and RL training throughput.However, because on-policy RL couples optimization with exploration, naively penalizing length can destabilize training and suppress exploration.To impose length pressure safely, we propose a lazy length penalty integrated into the rule-based RL pipeline: it activates only on correct trajectories, only after training accuracy enters a stably improving regime, and only when responses exceed a tolerance band beyond the minimal correct length.Across four settings, our method significantly reduces response length without extra training stages while maintaining or improving performance.In a logic reasoning setting, we achieve a 40% reduction in stepaveraged response length alongside a 14-point gain in performance.For math problems, we reduce step-averaged response length by 33% while preserving performance.