Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG
arXiv:2607.02966v2 Announce Type: replace Abstract: Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved passages remain English. In this setting, generation can fail despite strong base models: English evidence induces language drift (English or code-switching outputs) and models use evidence unreliably when producing non-English answers. We attribute these failures to two post-training challenges: (i) errors are prefix-dependent, so fixed-trajectory supervision suffers from prefix mismatch; an
The proliferation of RAG systems in diverse language contexts is exposing critical limitations in current cross-lingual AI architectures, particularly for non-English outputs.
This research addresses a key technical hurdle for deploying AI agents and RAG systems globally, improving their reliability and effectiveness in multilingual environments beyond English.
The proposed teacher-regularized reinforcement learning approach offers a method to mitigate language drift and unreliable evidence use in cross-lingual RAG, enhancing output quality for non-English users.
- · AI developers
- · Multilingual businesses
- · Users of non-English AI applications
- · Monolingual AI solutions
- · Legacy RAG implementations
Cross-lingual RAG systems will produce more accurate and contextually appropriate non-English outputs.
This improved reliability could accelerate the adoption of AI agents in non-English speaking markets, potentially leading to increased localization efforts.
Enhanced cross-lingual AI capabilities might contribute to more equitable global access to advanced AI tools, reducing the dominance of English-centric AI applications.
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