TRUST-SQL-8B
Overview
TrustSQL-8B is a fine-tuned Text-to-SQL model based on Qwen3-8B, introduced in TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas. The model is trained with multi-turn reinforcement learning and tool integration for Text-to-SQL over unknown database schemas.
Model details
- Base model:
Qwen/Qwen3-8B - Architecture:
Qwen3ForCausalLM - Parameters: 8.2B
- Hidden size: 4096
- Layers: 36
- Attention heads: 32 Q heads / 8 KV heads
- Context length: 40,960 tokens
- Precision: bfloat16
Models
| Model | Base | Link |
|---|---|---|
| TrustSQL-4B | Qwen3-4B | AIJian/TrustSQL-4B |
| TrustSQL-8B | Qwen3-8B | AIJian/TrustSQL-8B |
Training
TrustSQL follows a two-stage training pipeline: SFT warm-up followed by Phase-Aware GRPO optimization. The interaction protocol is Explore → Propose → Generate → Confirm.
Reported results
All results are reported under the Unknown Schema setting.
| Benchmark | Greedy | Majority voting |
|---|---|---|
| BIRD-Dev | 65.8 | 67.7 |
| Spider-Test | 83.9 | 86.5 |
| Spider-DK | 72.1 | 75.7 |
| Spider-Syn | 75.4 | 77.4 |
| Spider-Realistic | 82.1 | 84.1 |
The model reaches 65.8 EX on BIRD-Dev with greedy decoding and 67.7 EX with majority voting.
Recommended inference setup
This model is intended to run inside a tool-using agent loop:
- Explore database metadata and inspect relevant values.
- Propose only tables and columns verified by tool output.
- Generate and execute a candidate SQL query.
- Confirm the final answer, or return to exploration when evidence is insufficient.
The explicit proposal checkpoint is important: it helps prevent schema hallucination and provides a boundary for the phase-aware training objective.
Loading
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "AIJian/TrustSQL-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
For prompts, tool schemas, evaluation scripts, and training details, see https://github.com/JaneEyre0530/TrustSQL.
Limitations
This checkpoint was trained and evaluated with SQLite-based benchmarks. Inference requires a live and safe metadata/execution environment. Always validate generated SQL and use read-only permissions unless write access is explicitly required and controlled.
Citation
@article{jian2026trustsql,
title = {TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas},
author = {Jian, Ai and Zhang, Xiaoyun and Du, Wanrou and Ruan, Jingqing and Pei, Jiangbo and Zhang, Weipeng and Zeng, Ke and Cai, Xunliang},
journal = {arXiv preprint arXiv:2603.16448},
year = {2026}
}
License
This project is licensed under the Apache 2.0 License. See the LICENSE file for details.