Implicit Reasoning in Transformers is Reasoning through Shortcuts (2025)

Abstract: Test-time compute is emerging as a new paradigm for enhancing language models’ complex multi-step reasoning capabilities, as demonstrated by the success of OpenAI’s o1 and o3, as well as DeepSeek’s R1. Compared to explicit reasoning in test-time compute, implicit reasoning is more inference-efficient, requiring fewer generated tokens. However, why does the advanced reasoning capability fail to emerge in the implicit reasoning style? In this work, we train GPT-2 from scratch on a curated multi-step mathematical reasoning dataset and conduct analytical experiments to investigate how language models perform implicit reasoning in multi-step tasks. Our findings reveal: 1) Language models can perform step-by-step reasoning and achieve high accuracy in both in-domain and out-of-domain tests via implicit reasoning. However, this capability only emerges when trained on fixed-pattern data. 2) Conversely, implicit reasoning abilities emerging from training on unfixed-pattern data tend to overfit a specific pattern and fail to generalize further. Notably, this limitation is also observed in state-of-the-art large language models. These findings suggest that language models acquire implicit reasoning through shortcut learning, enabling strong performance on tasks with similar patterns while lacking generalization. Resources are available at https://github.com/TianheL/LM-Implicit-Reasoning.

## introduction chain-of-thought (cot; @xcite ) has sparked the development of explicit reasoning in large language models (llms). the subsequent rise of large reasoning models @xcite @xcite based on long cot demonstrates impressive capabilities across various tasks @xcite @xcite . recent works have shown * corresponding author. while recent advances in mechanistic interpretability have aimed to demystify the implicit reasoning processes of language models (lms), most studies are limited to single-step reasoning @xcite @xcite , which does not meet the expectation for handling complex reasoning tasks, such as advanced mathematical problems. meanwhile, for multi-step implicit reasoning, previous work primarily focuses on reasoning over factual knowledge @xcite , which may be hindered by issues such as inflated reasoning performance due to memorizing entity co-occurrences in the pretraining data @xcite @xcite . in this paper, to minimize the impact of memorization and investigate the underlying reasoning mechanisms, we explore implicit reasoning through the lens of mathematical problems. mathematical reasoning primarily depends on arithmetic operations that follow strict logical rules, which require algebraic manipulation based on specific operators and operands rather than recalling pre-trained knowledge like entity relationships. given that the strength of explicit reasoning stems from stepwise rationales, the first question we seek to address is rq1: can language models perform stepwise reasoning internally? to investigate this, we train gpt-2 from scratch on our synthetic multi- ## how do language models reason internally when the premise order is not fixed? based on the above findings, we find that transformers are able to perform step-by-step reasoning internally when the premise order is fixed. however, in complex reasoning tasks, the premises are not always presented sequentially; they may appear in a random order, requiring lms to organize the information internally. can language models still perform implicit reasoning step by step when the premises are shuffled? setup. for consistency, we continue to use the original data but randomly shuffle the order of premises 5 , excluding the question. to assess the impact of premise order, we study three different orders, including forward, reverse, and random. specifically, for the reverse order, we list the premises in reverse order; for the random order, we shuffle the premises randomly. ## conclusion in this paper, we investigate the implicit reasoning mechanism to uncover why advanced reasoning capabilities fail to emerge in the implicit reasoning style. we find that language models rely on shortcuts for implicit reasoning, and these shortcuts only work when the training data aligns with a specific pattern that supports directly chaining numbers. as a result, language models struggle with the " variable as subtrahend plight," which requires true reasoning capabilities, such as variable tracking and step-by-step computation, where shortcuts are no longer effective. experiments with current sota llms further validate our findings. we hope this work deepens the understanding of implicit reasoning limitations in lms and sparks future research to address lms' key challenges in implicit multi-step reasoning.