mlsanityData to 7 Oct 2026

On the Token Value Inequality in Efficient Reasoning

arXiv 2609.33970 ↗First posted 27 Sep 2026cs.CL cs.AIcited by 0

On the map: LLM reasoning → Prompting and chain-of-thought reasoning also fits: Test-time compute and efficient thinking

Abstract (arXiv)

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/.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 58 of its 97 references that are in the map. First posted on arXiv 27 Sep 2026.

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58 of 97 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Chain of Draft: Thinking Faster by Writing Less25 Feb
  2. 2024Training Large Language Models to Reason in a Continuous Latent Space9 Dec
  3. 2024From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step23 May
  4. 2024Let's Think Dot by Dot: Hidden Computation in Transformer Language Models24 Apr

Other references · 54

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