Qwen3-4B-tau2-grpo-v1

A 4B parameter model fine-tuned for multi-turn tool-use tasks, achieving 59% Pass@4 on tau2-bench (test split). This represents a 4x improvement over the base model and demonstrates that progressive training (SFT -> RFT -> GRPO) works effectively for complex, multi-turn agent tasks.

Model Description

This model was trained using a three-stage pipeline:

1. SFT (Supervised Fine-Tuning): Learning protocol and tool schemas from successful trajectories
2. RFT (Rejection Fine-Tuning): Concentrating training on high-quality rollouts via rejection sampling
3. GRPO (Group Relative Policy Optimization): Reinforcement learning with turn-level reward shaping

The training process is documented in the tau2 training cookbook.

Performance

tau2-bench Test Split (Pass@4 evaluation)

Domain	Pass@1	Pass@4	Tasks
Overall	36.0%	59.0%	100
Airline	15.0%	45.0%	20
Retail	55.0%	85.0%	40
Telecom	27.5%	40.0%	40

Training Progression

Stage	Overall Pass@4
Baseline (Qwen3-4B-Instruct)	14.3%
SFT + RFT	27.0%
GRPO (this model)	59.0%

Eval config: temperature=0.8, top_p=1.0, top_k=20, num_samples=4, TAU2_USER_MODEL=gpt-4.1-mini, TAU2_USER_TEMPERATURE=0.7, TAU2_MAX_STEPS=100.

Usage

With SGLang (recommended for evaluation)

# Start the server (use --tp 1 for single GPU)
python -m sglang.launch_server \
  --model-path Jarrodbarnes/Qwen3-4B-tau2-grpo-v1 \
  --host 0.0.0.0 --port 30000 --tp 2 --mem-fraction-static 0.70

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Jarrodbarnes/Qwen3-4B-tau2-grpo-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

Function Calling Format

This model uses Qwen3 native function calling format:

<tool_call>{"name": "tool_name", "arguments": {"arg": "value"}}</tool_call>

Include </tool_call> in stop sequences for proper parsing.

Training Details

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Training framework: SLIME (Megatron-LM + SGLang)
• SFT data: tau2-sft-seed-v3
• GRPO steps: 21 optimizer steps
• Reward shaping: Turn-level partial scores from tau2-bench reward_info
• User simulator (training): Local Qwen3-4B-Instruct on port 30001
• User simulator (eval): GPT-4.1-mini via OpenAI API

W&B Training Logs

• SFT run: b7d80rfe
• GRPO run: pkeu9kck

Resources

• Training Cookbook - Full methodology and reproduction steps
• SFT Checkpoint - Intermediate SFT+RFT checkpoint
• Training Dataset - Filtered RFT trajectories
• tau2-bench - Benchmark repository

Limitations

• Telecom domain: Dual-control tasks (where the agent must instruct rather than execute) remain challenging (40% Pass@4)
• User simulator sensitivity: Results vary with user simulator choice; GPT-4.1-mini recommended for reproducibility
• Pass@k vs Pass^k: This model reports pass@k (any success in k attempts), not the pass^k metric used on the official tau2-bench leaderboard

Citation

@misc{qwen3-tau2-grpo,
  title={Qwen3-4B-tau2-grpo-v1: Multi-Turn Tool-Use Agent via Progressive RL Training},
  author={Jarrod Barnes},
  year={2025},
  url={https://huggingface.co/Jarrodbarnes/Qwen3-4B-tau2-grpo-v1}
}

Acknowledgments

• Qwen Team for the base model
• Sierra Research for tau2-bench
• THUDM for the SLIME training framework