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RuishanFang
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Model Details
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RuishanFang
Model Tree
Base
Qwen/Qwen3-4B
Fine-tuned
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
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The Qwen3-4B-RODS model is a high-performance Large Language Model (LLM) fine-tuned for complex, multi-turn Function Calling (FC) and agentic tool-use tasks. Built upon the Qwen3-4B-Instruct base model, it has been trained using the novel RODS (Reward-driven Online Data Synthesis) framework combined with GRPO reinforcement learning.
RODS closes the loop between RL training and data generation: it repurposes the progress reward variance as a zero-cost capability boundary detector, continuously synthesizes structurally isomorphic training data at the agent's learning frontier, and manages a dynamic replay buffer that co-evolves with the policy. Starting from only 400 human-annotated seeds, RODS achieves strong multi-turn tool-use performance with extreme data efficiency.
The model was evaluated on the Berkeley Function-Calling Leaderboard (BFCL).
| Model | Size | Multi-Turn (Overall) | Base | Miss Func | Miss Param | Long Context |
|---|---|---|---|---|---|---|
| Qwen3-4B-Instruct (Base) | 4B | 22.13 | 26.50 | 21.00 | 15.50 | 25.50 |
| Qwen3-4B + RODS (ours) | 4B | 56.00 | 68.00 | 59.00 | 44.00 | 53.00 |
| Claude-Sonnet-4-5-20250929 | - | 61.38 | 69.00 | 65.00 | 52.50 | 59.00 |
RODS is a closed-loop RL-data synthesis framework with three co-evolving modules:
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "RuishanFang/Qwen3-4B-RODS"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
For tool-use inference, follow the Qwen3 function calling format. The model expects tools to be provided in the system prompt and generates structured <tool_call> responses.
This work is part of the open-source project AWorld, InclusionAI.
If you use RODS in your research, please cite:
bibtex
@article{fang2026rods,title={RODS: Reward-Driven Online Data Synthesis for Multi-Turn Tool-Use Agents},author={Fang, Ruishan and Lu, Siyuan and Zhuang, Chenyi and Lin, Tao},journal={arXiv preprint arXiv:2606.19047},year={2026}}
For inquiries, please contact:
fangruishan@westlake.edu.cn| Grok-4-1-fast-reasoning | - | 58.88 | 70.50 | 59.50 | 43.00 | 62.50 |
| Kimi-K2-Instruct | 1043B | 50.63 | 62.00 | 41.00 | 44.50 | 55.00 |
| Qwen3-32B | 32B | 47.88 | 56.00 | 52.50 | 40.00 | 43.00 |
| DeepSeek-V3.2-Exp | 671B | 44.88 | 55.00 | 49.00 | 27.00 | 48.50 |
| GPT-4o-2024-11-20 | - | 42.50 | 55.50 | 34.50 | 29.00 | 51.00 |