Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty
Jeonghye Kim, Xufang Luo, Minbeom Kim, Sangmook Lee, Dongsheng Li, Yuqing Yang
Why It Matters
What makes this one worth your time
Understanding and improving reasoning in LLMs is crucial for enhancing their reliability and performance in real-world applications.
The paper proposes a framework for understanding LLM reasoning as strategic information allocation under uncertainty.
Summary
The paper introduces an information-theoretic framework to understand reasoning in large language models (LLMs) by separating reasoning into procedural advancement and epistemic verbalization, which involves externalizing uncertainty. It suggests that sporadic verbalization can help LLMs recover from incorrect trajectories without explicit error triggers, and small-scale supervised fine-tuning (SFT) can instill or suppress this capability.
Key contributions
- Introduction of an information-theoretic framework for reasoning in LLMs.
- Empirical evidence that minimal doubt cues can recover failed reasoning trajectories.
- Demonstration that small-scale SFT can control the externalization of uncertainty in LLMs.
Notable insights
- Reasoning in LLMs can be improved by externalizing uncertainty through verbalization.
- Small-scale supervised fine-tuning can effectively instill or suppress the capability of uncertainty verbalization in LLMs.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2603.15500v2 Announce Type: replace Abstract: LLMs often exhibit Aha moments such as self-correction after tokens like "Wait," yet the underlying mechanism remains unclear. Standard LLMs collapse mainly through silent divergence, where trajectories drift from the correct answer yet remain locally coherent, so no explicit error triggers reactive self-correction. We introduce an information-theoretic framework that separates reasoning into procedural advancement and epistemic verbalization, the token-level externalization of uncertainty, and prove that sporadic verbalization restores convergence toward the correct answer even without explicit error triggers. Empirically, a minimal doubt cue recovers failed trajectories, and small-scale SFT suffices to instill or suppress this capability, suggesting that strong reasoning hinges less on an extraordinary inner mechanism than on the linguistic habit of externalizing uncertainty. Our framework recasts reasoning as strategic information allocation under uncertainty, offering a new lens for understanding and advancing LLM reasoning.