Reasoning Can Be Restored by Correcting a Few Decision Tokens
A recent study explores the reasoning gap between large reasoning models and base models in artificial intelligence. The research identifies that a small number of early decision tokens significantly impact the reasoning performance. By implementing a token intervention strategy, the study demonstrates that it is possible to enhance reasoning capabilities effectively.
- ▪Large reasoning models outperform base models on reasoning benchmarks, but the reasons for this gap are not well understood.
- ▪The study finds that only about 8% of generated tokens account for the main disagreement between models, focusing on early planning-related decisions.
- ▪The proposed intervention strategy allows for a one-token takeover by the reasoning model at high-disagreement positions, improving performance.
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Computer Science > Artificial Intelligence arXiv:2605.16874 (cs) [Submitted on 16 May 2026] Title:Reasoning Can Be Restored by Correcting a Few Decision Tokens Authors:Changshuo Shen, Leheng Sheng, Yuxin Chen, An Zhang, Xiang Wang View a PDF of the paper titled Reasoning Can Be Restored by Correcting a Few Decision Tokens, by Changshuo Shen and 4 other authors View PDF HTML (experimental) Abstract:Large reasoning models (LRMs) substantially outperform their base LLM counterparts on challenging reasoning benchmarks, yet it remains poorly understood where base models go wrong during token-by-token generation and how to narrow this gap efficiently.
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