EnvScaler-Qwen3-1.7B

EnvScaler-Qwen3-1.7B

Model Description

EnvScaler-Qwen3-1.7B is a tool-enhanced language model based on Qwen3-1.7B (Thinking Mode), trained using the EnvScaler framework for tool-interactive agent tasks. This model has been trained through Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL).

Training Process

This model was trained using a two-stage approach:

Stage 1: Supervised Fine-Tuning (SFT)

• Training Data: 9,022 trajectories from agent-environment interactions
• Data Source: EnvScaler-SFT-Traj-9K
• Scenarios: 4,684 SFT scenarios from EnvScaler-SFT-Scenario
• Environments: 141 synthesized environments from EnvScaler-191-Env

Stage 2: Reinforcement Learning (RL)

• Training Data: 2,550 RL scenarios from EnvScaler-RL-Scenario
• Environments: 50 synthesized environments from EnvScaler-191-Env
• Framework: Based on the ROLL framework

The training process enables the model to learn from both demonstration trajectories (SFT) and reinforcement signals (RL), resulting in improved performance on complex tool-interactive tasks.

How to Use

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "XXHStudyHard/EnvScaler-Qwen3-1.7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Use with function calling interface
# See EnvScaler project for full interaction examples

With EnvScaler Framework

For full integration with tool-interactive environments, please refer to the EnvScaler project documentation.

Related Resources

• Project Homepage: EnvScaler GitHub
• Paper: arXiv
• Training Datasets:
  • EnvScaler-191-Env
  • EnvScaler-SFT-Scenario
  • EnvScaler-RL-Scenario
  • EnvScaler-SFT-Traj-9K
• Other Models:
  • EnvScaler-Qwen3-1.7B
  • EnvScaler-Qwen3-4B
  • EnvScaler-Qwen3-8B

Citation

If you use this model, please cite our work:

@article{song2026envscaler,
  title={EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis},
  author={Song, Xiaoshuai and Chang, Haofei and Dong, Guanting and Zhu, Yutao and Dou, Zhicheng and Wen, Ji-Rong},
  journal={arXiv preprint arXiv:2601.05808},
  year={2026}
}

License

This model is licensed under the Apache 2.0 License, following the base Qwen3 model license.

Contact

For any questions or feedback, please contact: [email protected]