Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models
Long-form chain-of-thought reasoning can improve LLM performance on complex tasks, but models often continue generating unnecessary reasoning after a correct answer has emerged. We refer to this behavior as overthinking. We study this phenomenon from the perspective of GRPO-style reinforcement learning (RL) post-training, framing it as a training-time credit-assignment problem rather than merely a decoding-time stopping problem. In rollouts sampled at the onset of GRPO training, we observe that successful trajectories can exhibit a slightly higher degree of overthinking than unsuccessful trajectories for the same prompts. This early imbalance provides a starting point for an undesirable f...
| Authors | Zihao Wei, Wenjie Shi, Liang Pang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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