SQL-R1: NL2SQL model based on the NL2SQL emissions from the main systems in the complex exams with the visible sql generation

The nature of the creative language to the databases is growing in artificial spy, especially because it allows users to interact with systematic information using good language. The area, commonly known as NL2SQL (Natural Language in SQL), focusing on converting user variables to SQL commands that are not killed directly to the information. The aim is to facilitate non-technical access data and extend the use of data programs in various fields such as financial such as financial, health and sales. With the increase in llms, great progress has made these changes more accurate and able, especially when dealing with simple questions or formal data structures.

Despite the progress, changing the environmental language is always difficult in complex situations that join the other countries, nested questions, or beginners' semantics. The challenge is not just about producing the appropriate SQL but to generate the user's purpose properly and may be integrated. The common ways are striving to measure the high flooding territories where translation and accuracy are important. In addition, many models are now dependent on fixed schemas and training data, which affect their performance in new areas or from.

Most NL2SQL programs today depend on the planned guidelines, where large models of language are in Datable datasets with the relevant SQL response questions. While this approach leads to visible development, it launches limits in revision and translation. Because these types are organized in certain datasets and schemes, they often fail in unknown situations. Also, they follow a strong agricultural strategy, which can lead to failure in which the input deviate from training information. These programs are often unexpectedly lacking the processes of consultation, reducing their use in domains where lanes are needed.

Idea Research University of Science and Technology (Guangzhou), at the University of Chinese Academy of Science, and the Datarc Tech Ltd. Introduction Sql-r1. This new NL2SQL model receives the validity of learning validity than traditional readers. SQL-R1 uses respondents during training to improve its effectiveness. Instead of learning to the examples described, the model learns by producing SQL elections, removing them, and received a structured response. This response includes whether the SQL was directly good, even if it produced the right result, and how it applies and how it works. This powerful learning process allows the model to prepare its SQL Generation strategies later and improve normal performance in complex or unusual conditions.

Building SQL-R1, researchers begin to make good guidance in 200,000 are deducted from the largest performance data called Synsql-2.5m. This process, known as a cold start, verifies the model may follow the basic instructions and generate a simple SQL output. Following this, it is made to verify using the Group's ALGORITHM (GRPO). The model that produces a lot of SQL's SQL for each question and was rewarded based on the goal of scoring. This work has entered four metrics: format reward (+1 or -1 depending on the accuracy of the syntax), the Rewarding of the Rewarding), and the failure of the question), and the negative effects based on the depths and a consultation process. Each of these scores offered up the process of making the model decisions of the model.

SQL-R1 was examined in the two common benchmarks of NL2SQL Cardsmarks: spider and bird. The appointment of spider Development, the model received 87.6 accuracy of the other, and a set of spider, has received 88.7%. For details of birds, including 95 information from 37 domains, the model check 66.6%. These results are competitive or higher than large models, including closed source solutions such as GPT-4. Significantly, SQL-R1 has used the QWEN2.5-Coder-Coder-7b model, more than many other forms, indicates that higher accuracy can be obtained by practical politics in combination of effective learning. Residual research confirmed the offer for each reward component. Removing the format reward, for example, created accuracy decrease from 63.1% to 60.4%. To delete the results reward causing 0.7% decrease, which indicates that each item at a reward wedding plays a role in directing the model.

Several hints from the SQL-R1 survey:

• SQL-R1 has received 88.7% accurate test of spider test and 66.6% on a bird development set, using only 7B basic model (QWEN2.5-code-7b model).
• The model used in 200,000 samples from Synsql-2.5m targets to target beauty and complicated sample samples.
• GRPO Algorithm strengthens the strengthening power that strengthens energy, which requires no amount of model and work well with performance-related scores.
• The reward work has entered four components: Format (+ 1/11), execution (+ 2/2 / -3), and lengthy).
• The largest SQL-R1 models are prominent as GPT-4, highlighting that model state and the training of sensitive feedback as size.
• Cleaning courses reveal the importance of each reward: Removing the format reward causing a 2.7% decrease in operation, while removing the reward of a 2.4%.
• The way promotes clarity, as the model gives traces to which you make use of ''and'Tags, to improve the final user interpretation.

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