Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts

Lucas Bandarkar; Alan Ansell; Trevor Cohn

cs.CLMar 17, 2026

Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts

Authors: Lucas Bandarkar, Alan Ansell, Trevor Cohn

Organizations: University of California, Los Angeles · Google Research · University of Melbourne

Abstract

In this work, we analyze shortcomings in cross-lingual knowledge transfer in large, modern reasoning LLMs. We demonstrate that the perceived gap in knowledge transfer is primarily a script barrier. First, we conduct an observational data analysis on the performance of thinking models on two datasets with local knowledge from around the world, ECLeKTic and MultiLoKo. Our regression analysis shows that script match - not language or family - is the primary predictor of knowledge transfer failure once model capability and question difficulty are accounted for. We further this finding by providing the LLMs with the key entities of the questions in their source language and find that this disproportionately improves cross-script questions. We then posit that these LLMs could be reasoning better at test-time. To evaluate this, we develop a synthetic generation pipeline to design SFT samples to encourage the model to better reason about transliteration ambiguities when trying to fetch parametric knowledge at inference-time. We show that teaching two models to reason better reduces the cross-script transfer gap. As a result, we conclude that there is potential to improve cross-lingual parametric knowledge transfer during post-training.

Figures & tables

Variable	ECleKTic	MultiLoKo
OLS Intercept	$-$ 0.350	$-$ 0.439
Belebele Score	0.999	0.937
Script Match	0.121	0.133
Language Family	n.s.	n.s.
In-Language	n.s.	n.s.
Model Indicators (vs. Qwen3-30B)

Table 1: OLS Coefficients (

\beta

) when trying to predict the binary score of a question, with no scaling . n.s. denotes statistically insignificant (

\beta

);

p>0.05

Condition	All	Non- Latin	Latin	Script $\neq$	Script =
GPT-OSS-120B
Original	20.6	16.3	23.6	18.5	24.6
Prefix, Control	21.2	17.9	23.5	19.3	24.8
Prefix w/ src Ent.	22.2	19.4	24.2	20.7	25.1
$\Delta$	+1.0	+1.5	+0.7	+1.4	+0.2
Qwen3-235B-A22B

Table 2: Impact of our think-prefixing on ECLeKTic accuracy

\uparrow

. Script

\neq

denotes cross-script; Script

=

denotes same-script.

		ECLeKTic (12 languages)							MultiLoKo (31 languages)
			Latin		Resource		Script			Latin		Resource		Script
Model	Run	All	No	Latin	Low	High	$\neq$	=	All	No	Latin	Low	High	$\neq$	=
GPT-OSS-120B	Base	20.6	16.3	23.6	12.6	23.9	18.5	24.6	24.4	18.0	31.2	17.0	27.8	21.6	27.3
GPT-OSS-120B	LoRA	21.6	18.6	23.8	15.3	24.0	20.1	24.7	25.0	18.7	32.0	17.7	28.3	22.3	27.4
Qwen3-30B-A3B	Base	18.1	15.1	20.1	12.2	20.0	16.9	20.3	25.6	21.4	30.1	19.6	27.1	21.1	28.0
Qwen3-30B-A3B	LoRA	18.9	16.3	20.8	12.8	18.2	18.0	20.6	26.0	21.9	30.4	20.2	27.4	21.7	28.2

Table 3: Comprehensive evaluation of Base vs. SFT across two datasets. We compare performance across Latinity (Non-Latin vs. Latin), Resource levels (Low vs. High), and Script matching (

\neq

vs.

=

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Variable	ECleKTic	MultiLoKo
OLS Intercept	0.452	0.569
Belebele Score	0.088	0.100
Script Match	0.056	0.064
Language Family	n.s.	n.s.
In-Language	n.s.	n.s.
Model Indicators (vs. Qwen3-30B)

Appendix

Table 4: OLS Coefficients (

\beta

) when trying to predict the binary score of a question, after features are standard-scaled . n.s. denotes statistically insignificant coefficients;

p>0.05

. The values represent the expected increase in probability from an increase in one standard deviation of the feature. It allows for comparison between continuous features (e.g. Belebele score) and the rest. The value is essentially a metric for how much variance the feature encapsulates.

Hyperparam.	GPT-OSS-120B	Qwen3-30B-A3B
Num. Samples	599	387
Target Modules	All Attn	All Attn
LoRA Rank ( $r$ )	32	32
LoRA Dropout	0	0.05
Learning Rate	$2\times 10^{-5}$	$6\times 10^{-5}$
Epochs	8	8

Appendix

Table 5: Hyperparameters for the LoRA training runs for which results are reported in Table 3 .

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