---
type: concept
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  pageDirectory: concepts
  sources:
    - distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md
  confidence: 0.88
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - multilingual-chain-of-thought-fine-tuning
    - MCF
  citations:
    - file: distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md
title: Multilingual Chain-of-Thought Fine-Tuning
description: Fine-tuning language models on multilingual datasets like HuggingFaceH4/Multilingual-Thinking to enable chain-of-thought reasoning across languages
tags:
  - machine-learning
  - nlp
  - fine-tuning
  - multilingual
timestamp: "2026-06-19T15:13:58.451Z"
---

# Multilingual Chain-of-Thought Fine-Tuning

**Multilingual chain-of-thought fine-tuning** is a training approach where a large language model (LLM) is fine-tuned to perform internal reasoning in one language while receiving prompts and generating responses in a different language. This technique is enabled by specialized datasets containing translated chain-of-thought reasoning examples across multiple languages. ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

## Overview

Multilingual chain-of-thought fine-tuning enhances a model's ability to reason across languages by explicitly training it to generate intermediate reasoning steps in a specified language before producing the final answer. This capability is particularly valuable for deploying LLMs in multilingual contexts where users may submit prompts in one language while the model's internal reasoning is performed in another. ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

## Training Dataset

A key enabler of multilingual chain-of-thought fine-tuning is datasets like **HuggingFaceH4/Multilingual-Thinking**, which has been specifically curated with translated chain-of-thought reasoning examples in multiple languages. These datasets provide training pairs that teach the model to produce step-by-step reasoning in a designated language while responding to user queries in potentially different languages. ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

## Implementation

### System Prompt Control

The reasoning language is typically controlled through a system prompt. During training or inference, a system message specifies the language the model should use for its internal reasoning: ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

```python
REASONING_LANGUAGE = "German"
SYSTEM_PROMPT = f"reasoning language: {REASONING_LANGUAGE}"
```

### Example Behavior

After fine-tuning, a model trained with multilingual chain-of-thought data can respond to user prompts in one language while performing its reasoning in a different language specified in the system prompt. For example: ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

- **System prompt**: "reasoning language: German"
- **User prompt**: "¿Cuál es el capital de Australia?" (Spanish for "What is the capital of Australia?")
- The model's internal reasoning is performed in German, while the final answer can be provided in the user's language

## Technical Approach

### Parameter-Efficient Fine-Tuning

Multilingual chain-of-thought fine-tuning can be performed efficiently using [LoRA (Low-Rank Adaptation)](/concepts/lora-low-rank-adaptation.md) to train adapter layers while freezing the base model. This approach reduces memory requirements and training time while still achieving effective multilingual reasoning capabilities. ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

### Memory Optimization

Training large models with this technique can leverage [MXFP4 Quantization](/concepts/mxfp4-quantization.md) to reduce memory requirements during training, enabling fine-tuning of models with 20 billion or more parameters on fewer GPUs. ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

### Distributed Training

Multilingual chain-of-thought fine-tuning scales effectively with [distributed data parallelism](/concepts/distributed-data-parallel-ddp.md) across multiple GPUs. For example, training can be distributed across 8 H100 GPUs using serverless GPU compute. ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

## Distribution

The approach can be combined with the [@distributed Decorator](/concepts/distributed-decorator.md) from serverless GPU libraries to automatically provision multiple GPUs and handle data parallelism. For instance, training the OpenAI gpt-oss-20b model on the Multilingual-Thinking dataset typically takes 30-60 minutes on 8 H100 GPUs. ^[distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md]

## Applications

Multilingual chain-of-thought fine-tuning is useful for:

- **Multilingual customer support**: Where agents need to reason in a resource-rich language but respond in the user's preferred language
- **Cross-lingual knowledge retrieval**: When a model needs to reason about information available in one language while serving users speaking another
- **Consistent reasoning quality**: Leveraging a language in which the model reasons more effectively, even when responding in another language

## Related Concepts

- Chain-of-thought prompting
- [LoRA (Low-Rank Adaptation)](/concepts/lora-low-rank-adaptation.md)
- [Parameter-efficient fine-tuning](/concepts/parameter-efficient-fine-tuning-peft.md)
- [Distributed training](/concepts/workload-yaml-for-distributed-training.md)
- Multilingual language models
- [[gpt-oss-20b]]

## Sources

- distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md

# Citations

1. [distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws.md](/references/distributed-fine-tuning-of-openai-gpt-oss-20b-databricks-on-aws-7ee24e1a.md)
