Reasoning Can Be Restored by Correcting a Few Decision Tokens

• URL: http://arxiv.org/abs/2605.16874v1
• Date: Sat, 16 May 2026 08:33:31 GMT
• Title: Reasoning Can Be Restored by Correcting a Few Decision Tokens
• Abstract summary: We study the base-reasoning gap through quantifying token-level distributional disagreement.<n>We find that the reasoning advantage is highly sparse and concentrates on a small set of early, planning-related decision tokens.<n>We propose disagreement-guided token intervention, a simple inference-time delegation scheme.
• Score: 23.834168837720796
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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 concentrate early in the response, are strongly enriched in planning-related decisions (17x), and coincide with high base-model uncertainty -- suggesting that base models fail mainly at early planning points that steer the subsequent reasoning trajectory. Building on these findings, we propose disagreement-guided token intervention, a simple inference-time delegation scheme that performs a one-token takeover by the reasoning model only at high-disagreement positions and immediately switches back to the base model. With a small intervention budget, this sparse delegation substantially recovers and can even surpass the performance of a same-size reasoning model on challenging reasoning tasks. Code is available at https://github.com/AlphaLab-USTC/RRTokenIntervention.

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