Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG
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; and (ii) sequence-level (partly discrete / judge-based) rewards yield noisy credit assignment and high-variance updates. We propose TR-RAG, a teacher-r...
| Authors | Haotian Zhou, Weiran Huang, Siqi Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 July 2026 |
| Last updated at source | 9 July 2026 |
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