TokenProbe improves reasoning efficiency by cutting redundant tokens
On the Token Value Inequality in Efficient Reasoning
Computation and LanguageArtificial Intelligence
Summary
Thinking through complex problems with AI often takes a lot of words, and not all those words carry equal importance. The authors found that some words in AI explanations are crucial while others are just extra filler. They created TokenProbe, a way to recognize and remove these less helpful words without hurting the AI's reasoning. This method lets AI give answers using far fewer words and can even beat some top existing models when using the same word limit.
What this means in practice
- •For large language model engineers: Cut token use in model-generated reasoning traces to speed up inference while keeping accuracy.
- •For ai service providers: Reduce token consumption in customer-facing AI reasoning pipelines to lower operational costs significantly.$Commercial implications: Enables cost-effective deployment of language models by substantially decreasing token usage during reasoning.
Authors
Runjia Zeng, Hang Hua, Yiyang Liu, Zhiqiang Tao, Ruixiang Tang, Qifan Wang, Cheng Han, Dongfang Liu
Abstract
Chain-of-Thought reasoning has enabled large language models to achieve substantial performance gains on complex tasks. However, these gains come at the cost of dramatically increased token consumption. This raises a fundamental question: is every token in the reasoning trace equally valuable? We present a diagnostic and optimization framework grounded in a key empirical finding: the value of tokens within a CoT reasoning sequence is highly non-uniform, and this non-uniformity can be effectively characterized by token-level log probability signals. We show that normalized log probability helps distinguish core tokens, which carry structural and decisive reasoning content, from redundant tokens, which are exploratory, low-confidence filler that contributes less directly to the final answer. Building on these findings, we formulate the TokenProbe framework around two empirical findings and one claim: findings identify token value inequality first and then establish TokenProbe as a core-token proxy, and the claim introduces an efficient GRPO objective positing that selectively compressing redundant tokens can yield Pareto improvements in the accuracy-token efficiency space. Empirically, our method preserves reasoning quality while reducing the token usage by 76% of the baseline. Under matched reasoning-length budgets, we show that it can even outperform strong flagship baselines like Gemini-3.1-Pro. Homepage: https://runjia.tech/tokenprobe/.