Mitigating Overthinking in Large Reasoning Language Models via Reasoning Path Deviation Monitoring
Large Reasoning Language Models (LRLMs) demonstrate impressive capabilities on complex tasks by utilizing long Chain-of-Thought reasoning. However, they are prone to overthinking, which generates redundant reasoning steps that degrade both performance and efficiency. Recently, early-exit strategies are proposed to mitigate overthinking by dynamically and adaptively terminating redundant reasoning. However, current early-exit methods either introduce extra training overhead by relying on proxy models or limit inference throughput due to the frequent content switching between reasoning and generating probing answers. Moreover, most early-exit methods harm LRLMs performance due to over-trunc...
| Authors | Weixin Guan, Liang Li, Jiapeng Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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