This model is the backbone language model used in the paper "EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management". It is initialized from Qwen3-8B and further trained to support autonomous data science tasks through multi-agent supervised fine-tuning and reinforcement learning.

EvoDS is a self-evolving autonomous data science agent that continuously improves its capabilities over time. The framework introduces two key mechanisms:

ASA enables the agent to autonomously synthesize, validate, cache, and reuse executable skills, allowing the agent's action space to expand over time.

ACC enables efficient long-horizon reasoning by dynamically compressing interaction history and preserving critical information under limited context budgets.