In LLM Reasoning, there is Irrationality on top of Value Misalignment

arXiv:2606.20624 · cs.AI, cs.CL, cs.LG, stat.ML · Submitted 2026-05-26 · Read on arXiv

cs.AI, cs.CL, cs.LG, stat.ML

Submitted: 2026-05-26

Updated: 2026-08-31

Comments: Published in the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP) as a Main Conference paper

Code: https://github.com/EVIEHub/LLM-Rationality

License: http://creativecommons.org/licenses/by/4.0/

The gist: Significant progress has been made in aligning LLMs with target value functions.

Terminology

Abstract

Significant progress has been made in aligning LLMs with target value functions. We argue that, even when an LLM has been well aligned in (post-)training, it may still fail to maximise the aligned value in reasoning. We mathematically formalise this gap as rational value risk: the utility discrepancy between a model's deployed reasoning strategy and its rational counterpart whose responses maximise utility in the steepest direction. The estimation error of rational value risk is further decomposed into three components from bounded prompts, bounded responses, and imperfect verifiers. Extensive experiments are conducted, covering models Llama-3.1, Qwen-2.5, Tülu-3 families (7B-72B), GPT-5.2, GPT-5.5, and DeepSeek-V4, and benchmarks UltraFeedback, AlpacaEval, GSM8K, MATH, HumanEval, and MathArena. The results validate that (1) rational value risk is widespread; (2) value alignment can reduce, but cannot avoid, it; (3) self-consistency can improve rationality; and (4) a longer chain of thought improves rationality but with diminishing returns. The code is at https://github.com/EVIEHub/LLM-Rationality

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