# Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2601.14896 · HTML: https://mlsanity.com/paper/2601.14896/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)
- Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/)

## Builds on (most cited first, 23 of 23)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [XOR QA: Cross-lingual Open-Retrieval Question Answering](https://mlsanity.com/paper/2010.11856/) (2020)
- [When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation](https://mlsanity.com/paper/2402.11457/) (2024)
- [RATT: A Thought Structure for Coherent and Correct LLM Reasoning](https://mlsanity.com/paper/2406.02746/) (2024)
- [Investigating Language Preference of Multilingual RAG Systems](https://mlsanity.com/paper/2502.11175/) (2025)
- [Multilingual Knowledge Editing with Language-Agnostic Factual Neurons](https://mlsanity.com/paper/2406.16416/) (2024)
- [Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning](https://mlsanity.com/paper/2510.07300/) (2025)
- [Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques](https://mlsanity.com/paper/2310.10310/) (2023)
- [Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task](https://mlsanity.com/paper/2504.03616/) (2025)
- [Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts](https://mlsanity.com/paper/2505.22582/) (2025)
- [Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement](https://mlsanity.com/paper/2506.05154/) (2025)
- [OpenDecoder: Open Large Language Model Decoding to Incorporate Document Quality in RAG](https://mlsanity.com/paper/2601.09028/) (2026)
- [Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs](https://mlsanity.com/paper/2305.17740/) (2023)
- [Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning](https://mlsanity.com/paper/2508.17387/) (2025)
- [MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval](https://mlsanity.com/paper/2510.27569/) (2025)
- [ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering](https://mlsanity.com/paper/2510.13312/) (2025)
- [DMDTEval: An Evaluation and Analysis of LLMs on Disambiguation in Multi-domain Translation](https://mlsanity.com/paper/2504.20371/) (2025)
- [RECON: Reasoning with Condensation for Efficient Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.10448/) (2025)

## Built on by (most cited first, 2 of 2)

- [Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG](https://mlsanity.com/paper/2607.02966/) (2026)
- [Where's Waldo? Query-language Preference under Cross-lingual Knowledge Disparities](https://mlsanity.com/paper/2610.00606/) (2026)

## Abstract (arXiv)

Multilingual retrieval-augmented generation (MRAG) requires models to effectively acquire and integrate beneficial external knowledge from multilingual collections. However, most existing studies employ a unitive process where queries of equivalent semantics across different languages are processed through a single-turn retrieval and subsequent optimization. Such a ``one-size-fits-all'' strategy is often suboptimal in multilingual settings, as the models occur to knowledge bias and conflict during the interaction with the search engine. To alleviate the issues, we propose LcRL, a multilingual search-augmented reinforcement learning framework that integrates a language-coupled Group Relative Policy Optimization into the policy and reward models. We adopt the language-coupled group sampling in the rollout module to reduce knowledge bias, and regularize an auxiliary anti-consistency penalty in the reward models to mitigate the knowledge conflict. Experimental results demonstrate that LcRL not only achieves competitive performance but is also appropriate for various practical scenarios such as constrained training data and retrieval over collections encompassing a large number of languages. Our code is available at https://github.com/Cherry-qwq/LcRL-Open.