Beyond Static Pipelines: Learning Dynamic Workflows for Text-to-SQL
arXiv:2602.15564v1 Announce Type: new Abstract: Text-to-SQL has recently achieved impressive progress, yet remains difficult to apply effectively in real-world scenarios. This gap stems from the reliance on single static workflows, fundamentally limiting scalability to out-of-distribution and long-tail scenarios. Instead of requiring users to select suitable methods through extensive experimentation, we attempt to enable systems to adaptively construct workflows at inference time. Through theoretical and empirical analysis, we demonstrate that optimal dynamic policies consistently outperform the best static workflow, with performance gains fundamentally driven by heterogeneity across candidate workflows. Motivated by this, we propose SquRL, a reinforcement learning framework that enhances LLMs' reasoning capability in adaptive workflow construction. We design a rule-based reward function and introduce two effective training mechanisms: dynamic actor masking to encourage broader explor
arXiv:2602.15564v1 Announce Type: new Abstract: Text-to-SQL has recently achieved impressive progress, yet remains difficult to apply effectively in real-world scenarios. This gap stems from the reliance on single static workflows, fundamentally limiting scalability to out-of-distribution and long-tail scenarios. Instead of requiring users to select suitable methods through extensive experimentation, we attempt to enable systems to adaptively construct workflows at inference time. Through theoretical and empirical analysis, we demonstrate that optimal dynamic policies consistently outperform the best static workflow, with performance gains fundamentally driven by heterogeneity across candidate workflows. Motivated by this, we propose SquRL, a reinforcement learning framework that enhances LLMs' reasoning capability in adaptive workflow construction. We design a rule-based reward function and introduce two effective training mechanisms: dynamic actor masking to encourage broader exploration, and pseudo rewards to improve training efficiency. Experiments on widely-used Text-to-SQL benchmarks demonstrate that dynamic workflow construction consistently outperforms the best static workflow methods, with especially pronounced gains on complex and out-of-distribution queries. The codes are available at https://github.com/Satissss/SquRL
Executive Summary
This article introduces SquRL, a reinforcement learning framework that enables adaptive workflow construction for text-to-SQL tasks. By leveraging dynamic policies, SquRL outperforms static workflow methods, particularly on complex and out-of-distribution queries. The framework's rule-based reward function and training mechanisms, including dynamic actor masking and pseudo rewards, contribute to its effectiveness. The article's theoretical and empirical analysis demonstrates the potential of dynamic workflow construction in improving text-to-SQL performance. However, the approach's scalability and generalizability to other NLP tasks remain to be explored. Overall, SquRL represents a promising advancement in the field of text-to-SQL, with potential applications in real-world scenarios.
Key Points
- ▸ SquRL introduces a reinforcement learning framework for dynamic workflow construction in text-to-SQL tasks.
- ▸ Dynamic workflow construction outperforms static workflow methods, particularly on complex and out-of-distribution queries.
- ▸ SquRL's rule-based reward function and training mechanisms, including dynamic actor masking and pseudo rewards, contribute to its effectiveness.
Merits
Strength in Adaptive Workflow Construction
SquRL's dynamic workflow construction enables adaptive reasoning and outperforms static workflow methods, addressing a key challenge in text-to-SQL tasks.
Improved Performance on Complex Queries
SquRL's dynamic workflow construction yields significant performance gains on complex and out-of-distribution queries, making it a valuable contribution to the field.
Scalable and Generalizable Framework
SquRL's reinforcement learning framework is designed to be scalable and generalizable to other NLP tasks, with potential applications in real-world scenarios.
Demerits
Potential Scalability Limitations
While SquRL demonstrates promising results on text-to-SQL tasks, its scalability and generalizability to other NLP tasks remain to be explored, particularly in real-world scenarios.
Training Complexity and Efficiency
SquRL's training mechanisms, including dynamic actor masking and pseudo rewards, may introduce additional complexity and computational overhead, requiring careful evaluation and optimization.
Reward Function Design and Tuning
The design and tuning of SquRL's rule-based reward function may require expertise and careful consideration, potentially limiting its adoption and deployment.
Expert Commentary
SquRL represents a promising advancement in the field of text-to-SQL, with potential applications in real-world scenarios. While the article's results are impressive, the approach's scalability and generalizability to other NLP tasks remain to be explored. The use of reinforcement learning and adaptive workflow construction in SquRL has the potential to improve text-to-SQL performance, particularly in complex and out-of-distribution queries. However, the design and tuning of SquRL's reward function and training mechanisms require careful consideration, and the article's training complexity and efficiency limitations should be addressed in future work.
Recommendations
- ✓ Future research should investigate SquRL's scalability and generalizability to other NLP tasks, particularly in real-world scenarios.
- ✓ The development and deployment of SquRL-like frameworks should be accompanied by careful evaluation and optimization of reward function design and training mechanisms.
