Google提出DualSQL多智能体训练方法
Great paper from Google and colleagues. Trains Text-to-SQL agents using multi-agent RL. (bookmark ...
Google用多智能体RL训练Text-to-SQL，小模型也能超越大模型，还解决了训练崩溃问题。
Google研究人员提出DualSQL方法，将Text-to-SQL任务拆分为两个智能体：一个负责链接问题与正确表列，另一个负责编写SQL。这两个智能体共享相同模型权重，通过多智能体强化学习共同训练。DualSQL-4B模型在仅3,755个样本训练后，在BIRD dev基准上达到68.0%执行准确率，与早期7B模型相当；DualSQL-8B达到71.1%，超越32B参数的单模型系统。
Great paper from Google and colleagues. Trains Text-to-SQL agents using multi-agent RL. (bookmark ...
Great paper from Google and colleagues. Trains Text-to-SQL agents using multi-agent RL. (bookmark it) This work proposes DualSQL, which splits Text-to-SQL into two agents, one that links the question to the right tables and columns and one that writes the SQL. Both agents run on the same model weights, so a single multi-agent RL run trains both roles together. The agents can query the database through three tools while they reason. Multi-agent RL tends to collapse during training, so the authors add guardrails on rollouts and a new reward, robust execution match, that judges SQL correctness more accurately. Trained on only 3,755 examples, DualSQL-4B reaches 68.0% execution accuracy on BIRD dev, matching earlier 7B models. DualSQL-8B reaches 71.1%, ahead of previous single-model systems with 32B parameters. Paper: arxiv.org/abs/2609.18135 Chat with Paper: academy.dair.ai/papers/dualsql… 💬 0 🔄 0 ❤️ 1 👀 678 📊 1 ⚡
