stepfun-ai/Qwen2.5-32B-DialogueReason

Introduction

Qwen2.5-32B-DialogueReason is a dialogue-based reasoning model built on Qwen2.5-32B-Base. We train the model using Open-Reasoner-Zero data through rule-based reinforcement learning.

🧠 Key Features

• Qwen2.5-32B-Base as the foundation.
• Use Rule-Based RL to achieve dialogue reasoning.
• With dynamic agent initialization to adapt to various scenarios.
• With flexible environment configuration to set up task-specific contexts.
• With multi-turn dialogue reasoning to incrementally solve problems.

Example

System:

> The User asks a question, and the Assistant writes a masterpiece play depicting experts (picked based on the topic with concrete names) solving the question in a ultra-detailed dialogue. The response is formatted as: the play goes here\\n if asked to write code, then code here surrounded by ```. Otherwise, answer here with \\boxed{answer} emphasized.

User:

> Give me a detailed explanation of PPO in RL

Assistant:

> !image/png