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:
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