Principle:Volcengine Verl Prompt Template Design

Knowledge Sources	OpenAI Chat Completions API verl
Domains	Prompt_Engineering, NLP, Data_Engineering
Last Updated	2026-02-07 14:00 GMT

Overview

The construction of chat-formatted prompt templates that convert raw dataset questions into the OpenAI message format expected by language model tokenizers.

Description

Prompt Template Design converts raw questions or problems from datasets into the standardized chat message format (list of role/content dictionaries). This format is compatible with model tokenizers that use chat templates (e.g., tokenizer.apply_chat_template()).

Key aspects:

• Messages use the OpenAI format: [{"role": "system"|"user"|"assistant", "content": str}]
• Instruction suffixes guide the model's output format (e.g., "Put your final answer after ####" for GSM8K)
• Multi-turn prompts include system messages with tool instructions

Usage

Use prompt template design when converting any raw dataset for verl training. The template must match the model's expected chat format.

Theoretical Basis

Prompt templates are a pattern users implement per dataset:

# Abstract prompt template construction
def format_prompt(question, dataset_type):
    if dataset_type == "math":
        instruction = "Solve the problem. Put your final answer after ####"
        return [{"role": "user", "content": question + "\n" + instruction}]
    elif dataset_type == "multi_turn":
        return [
            {"role": "system", "content": tool_instructions},
            {"role": "user", "content": question}
        ]

Related Pages

Implemented By

• Implementation:Volcengine_Verl_Chat_Message_Template