Reasoning Can Be Restored by Correcting a Few Decision Tokens
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. We study the base-reasoning gap through quantifying token-level distributional disagreement between a base model and a stronger reasoning model using likelihood-based divergences. Across benchmarks, we find that the reasoning advantage is highly sparse and concentrates on a small set of early, planning-related decision tokens. For instance, on Qwen3-0.6B, only ~8% of generated tokens account for the salient disagreement, and these tokens co...
| Authors | Changshuo Shen, Leheng Sheng, Yuxin Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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