OpenAI's Preference Fine-Tuning: A Guide With Examples

OpenAI's Preference Fine-Tuning (PFT): A Guide to Aligning LLMs with User Preferences

Preference fine-tuning (PFT) is a powerful technique for aligning large language models (LLMs) with user preferences. Recently introduced by OpenAI, PFT complements supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) as a method to shape model outputs. This article provides a concise explanation of PFT and demonstrates its application using OpenAI's developer dashboard.

Understanding OpenAI's PFT

Unlike SFT, which focuses on generating specific outputs for given inputs, PFT aims to guide the model towards preferred responses while avoiding undesirable ones. Direct preference optimization (DPO), the core technique in OpenAI's PFT, is a simple yet effective alignment method. Unlike RLHF, DPO bypasses the complexity of reward models, directly optimizing a loss function. This simplifies implementation and improves computational efficiency.

The DPO dataset consists of paired responses for each prompt: one preferred and one non-preferred. For OpenAI's PFT, this dataset must be in JSONL format with the following structure:

{
  "input": {
    "messages": [
      {
        "role": "user",
        "content": "Prompt text here"
      }
    ],
    "tools": [],
    "parallel_tool_calls": true
  },
  "preferred_output": [
    {
      "role": "assistant",
      "content": "Preferred response here"
    }
  ],
  "non_preferred_output": [
    {
      "role": "assistant",
      "content": "Non-preferred response here"
    }
  ]
}

OpenAI recommends combining SFT and PFT for optimal alignment. PFT is typically applied after initial SFT on a supervised dataset.

Dataset Preparation for PFT

Creating a preference dataset involves generating pairs of LLM outputs (e.g., using different temperature settings) and then using another LLM (ideally a more powerful one) to label each pair as "preferred" and "non-preferred."

This tutorial uses a simplified approach: downloading a pre-existing preference dataset (e.g., argilla/ultrafeedback-binarized-preferences from Hugging Face) and restructuring the first 50 rows using a Python script. This script converts the dataset to the required JSONL format for OpenAI's PFT.

# ... (Python code to process and convert the Hugging Face dataset to OpenAI's JSONL format is omitted for brevity but described in the original article) ...

Remember to ensure your final dataset is in JSONL format and remove any trailing empty lines.

Running OpenAI's PFT

Once your dataset is ready:

1. Access the OpenAI dashboard.
2. Navigate to the fine-tuning section and initiate a new fine-tuning job.
3. Select "direct preference optimization" as the fine-tuning method.
4. Upload your prepared training and validation datasets (if available).

OpenAI allows customization of hyperparameters; however, you can let the system automatically determine optimal settings. Training time depends on the dataset size.

Conclusion

OpenAI's PFT, utilizing DPO, provides a valuable tool for refining LLM behavior and aligning it with user preferences. By carefully preparing the dataset in the specified JSONL format, you can leverage OpenAI's infrastructure to achieve a more tailored and desirable model response style. Further resources on OpenAI's fine-tuning methods, including SFT and RFT, are available in the original article's links.

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