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ARXIV:2604.02881 · LLM TRAINING · SUBMITTED 06 APR · 20:17 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02881LLM TRAININGSUBMITTED 06 APR · 20:17 UTCFRESHNESS UNKNOWNBaban Gain · Asif Ekbal · Trilok Nath Singh · arXiv
This research explains why merging fine-tuned language models fails for multilingual translation, identifying representational divergence as the cause.
Opportunity summary
Pain This research explains why merging fine-tuned language models fails for multilingual translation, identifying representational divergence as the cause.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
This research explains why merging fine-tuned language models fails for multilingual translation, identifying representational divergence as the cause. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood.
Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our work thus provides an explanation for why merging fails in multilingual translation scenarios.
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
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This research explains why merging fine-tuned language models fails for multilingual translation, identifying representational divergence as the cause.
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10.48550/arXiv.2604.02881This research explains why merging fine-tuned language models fails for multilingual translation, identifying representational divergence as the cause.
Abstract
Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood. We systematically study weight-space merging for multilingual machine translation by fully fine-tuning language model on large-scale bilingual corpora and evaluating standard merging strategies. Our experiments reveal that merging degrades performance, especially when target languages differ. To explain this failure, we analyze internal representations using span-conditioned neuron selectivity and layer-wise centered kernel alignment. We find that language-specific neurons concentrate in embedding layers and upper transformer blocks, while intermediate layers remain largely shared across languages. Critically, fine-tuning redistributes rather than sharpens language selectivity: neurons for supervised and related languages become less exclusive, while those for unsupervised languages grow more isolated. This redistribution increases representational divergence in higher layers that govern generation. These findings suggest that multilingual fine-tuning may reshape geometry in ways that reduce compatibility with standard weight-space merging assumptions. Our work thus provides an explanation for why merging fails in multilingual translation scenarios.
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PROBLEM
This research explains why merging fine-tuned language models fails for multilingual translation, identifying representational divergence as the cause. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood.
METHOD
Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our work thus provides an explanation for why merging fails in multilingual translation scenarios.
WHY NOW
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This research explains why merging fine-tuned language models fails for multilingual translation, identifying representational divergence as the cause. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our work thus provides an explanation for why merging fails in multilingual translation scenarios.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Training moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research explains why merging fine-tuned language models fails for multilingual translation, identifying representational divergence as the cause.
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