@inproceedings{he-etal-2026-grokking,
title = "Is Grokking Worthwhile? Functional Analysis and Transferability of Generalization Circuits in Transformers",
author = "He, Kaiyu and
Zhang, Mian and
Wu, Peilin and
Du, Xinya and
Chen, Zhiyu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1697/",
pages = "33993--34001",
ISBN = "979-8-89176-395-1",
abstract = "While Large Language Models (LLMs) excel at factual retrieval, they often struggle with the ``curse of two-hop reasoning'' in compositional tasks. Recent research suggests that parameter-sharing transformers can bridge this gap by forming a ``Generalization Circuit'' during a prolonged ``grokking'' phase. A fundamental question arises: Is a grokked model truly superior to its non-grokked counterparts? Furthermore, is the extensive computational cost of waiting for the grokking phase worthwhile? In this work, we conduct a mechanistic study to evaluate the Generalization Circuit{'}s role in knowledge assimilation and transfer. We demonstrate that: (i) The inference paths established by non-grokked and grokked models for in-distribution compositional queries are identical. This suggests that the ``Generalization Circuit'' does not represent the sudden acquisition of a new reasoning paradigm. Instead, we argue that grokking is the process of integrating memorized atomic facts into an easy-acquire, naturally established reasoning path. (ii) Achieving high accuracy on unseen cases after prolonged training and the formation of a certain reasoning path are not bound; they can occur independently under specific data regimes. (iii) Even a mature circuit exhibits limited transferability when integrating new knowledge, suggesting that ``grokked'' Transformers do not achieve a full mastery of compositional logic."
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%0 Conference Proceedings
%T Is Grokking Worthwhile? Functional Analysis and Transferability of Generalization Circuits in Transformers
%A He, Kaiyu
%A Zhang, Mian
%A Wu, Peilin
%A Du, Xinya
%A Chen, Zhiyu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F he-etal-2026-grokking
%X While Large Language Models (LLMs) excel at factual retrieval, they often struggle with the “curse of two-hop reasoning” in compositional tasks. Recent research suggests that parameter-sharing transformers can bridge this gap by forming a “Generalization Circuit” during a prolonged “grokking” phase. A fundamental question arises: Is a grokked model truly superior to its non-grokked counterparts? Furthermore, is the extensive computational cost of waiting for the grokking phase worthwhile? In this work, we conduct a mechanistic study to evaluate the Generalization Circuit’s role in knowledge assimilation and transfer. We demonstrate that: (i) The inference paths established by non-grokked and grokked models for in-distribution compositional queries are identical. This suggests that the “Generalization Circuit” does not represent the sudden acquisition of a new reasoning paradigm. Instead, we argue that grokking is the process of integrating memorized atomic facts into an easy-acquire, naturally established reasoning path. (ii) Achieving high accuracy on unseen cases after prolonged training and the formation of a certain reasoning path are not bound; they can occur independently under specific data regimes. (iii) Even a mature circuit exhibits limited transferability when integrating new knowledge, suggesting that “grokked” Transformers do not achieve a full mastery of compositional logic.
%U https://aclanthology.org/2026.findings-acl.1697/
%P 33993-34001
Markdown (Informal)
[Is Grokking Worthwhile? Functional Analysis and Transferability of Generalization Circuits in Transformers](https://aclanthology.org/2026.findings-acl.1697/) (He et al., Findings 2026)
ACL