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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
- tool-use
- multi-turn
- agents
- grpo
- reinforcement-learning
- tau2-bench
datasets:
- Jarrodbarnes/tau2-sft-seed-v3
pipeline_tag: text-generation
model-index:
- name: Qwen3-4B-tau2-grpo-v1
results:
- task:
type: multi-turn-tool-use
name: tau2-bench
dataset:
type: tau2-bench
name: tau2-bench (test split)
metrics:
- type: pass@1
value: 36.0
name: Pass@1 (Overall)
- type: pass@4
value: 59.0
name: Pass@4 (Overall)
---
# 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](https://github.com/THUDM/slime/blob/main/examples/tau-bench/training_cookbook.md).
## 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)
```bash
# 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
```python
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](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
- **Training framework**: [SLIME](https://github.com/THUDM/slime) (Megatron-LM + SGLang)
- **SFT data**: [tau2-sft-seed-v3](https://huggingface.co/datasets/Jarrodbarnes/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](https://wandb.ai/jbarnes850-near-protocol/tau2-cookbook/runs/b7d80rfe)
- GRPO run: [pkeu9kck](https://wandb.ai/jbarnes850-near-protocol/tau2-cookbook/runs/pkeu9kck)
## Resources
- [Training Cookbook](https://github.com/THUDM/slime/blob/main/examples/tau-bench/training_cookbook.md) - Full methodology and reproduction steps
- [SFT Checkpoint](https://huggingface.co/Jarrodbarnes/Qwen3-4B-tau2-sft1) - Intermediate SFT+RFT checkpoint
- [Training Dataset](https://huggingface.co/datasets/Jarrodbarnes/tau2-sft-seed-v3) - Filtered RFT trajectories
- [tau2-bench](https://github.com/sierra-research/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
```bibtex
@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](https://github.com/QwenLM/Qwen3) for the base model
- [Sierra Research](https://github.com/sierra-research/tau2-bench) for tau2-bench
- [THUDM](https://github.com/THUDM/slime) for the SLIME training framework

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"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9728,
"max_position_embeddings": 262144,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 5000000,
"sliding_window": null,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.51.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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"transformers_version": "4.51.0"
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