Executing as You Generate: Hiding Execution Latency in LLM Code Generation
Summary: arXiv:2604.00491v1 Announce Type: cross
Abstract: Current LLM-based coding agents follow a serial execution paradigm: the model first generates the complete code, then invokes an interpreter to execute it. This sequential workflow leaves the executor idle during generation and the generator idle during execution, resulting in unnecessary end-to-end latency. We observe that, unlike human developers, LLMs produce code tokens sequentially without revision, making it possible to execute code as it is being generated.
We formalize this parallel execution paradigm, modeling it as a three-stage pipeline of generation, detection, and execution, and derive closed-form latency bounds that characterize its speedup potential and operating regimes. We then present Eager, a concrete implementation featuring AST-based chunking, dynamic batching with gated execution, and early error interruption.
Introduction
The advent of large language models (LLMs) has transformed the landscape of code generation, enabling developers to harness the power of AI in their programming tasks. However, the traditional approach of code generation followed by execution is not efficient, leading to increased latency and reduced productivity.
Challenges in Current LLM-Based Coding Agents
Current methodologies for LLM-based coding agents are hindered by several challenges:
- Sequential Execution: The model generates the code in its entirety before it is executed, resulting in a delay.
- Idle Resources: The executor remains idle while the model generates code, wasting computational resources.
- Lack of Real-Time Feedback: There is no immediate feedback loop during the coding process, which can lead to errors being missed until the execution phase.
The Eager Implementation
Eager addresses these challenges by introducing a novel methodology that allows for parallel execution of code generation and execution. The implementation consists of:
- AST-Based Chunking: This technique breaks down the code into manageable chunks, enabling real-time execution as the code is generated.
- Dynamic Batching: Eager employs dynamic batching with gated execution to optimize resource usage and execution time.
- Early Error Interruption: This feature allows the system to detect and interrupt execution upon encountering an error, thereby reducing wasted computational effort.
Performance Evaluation
The performance of Eager was rigorously evaluated across various benchmarks, involving seven different LLMs and three execution environments. The results demonstrated substantial improvements in execution latency:
- Reduction in Non-Overlapped Execution Latency: Up to 99.9% reduction was observed, showcasing the efficacy of the parallel execution model.
- End-to-End Latency Improvement: Eager achieved a reduction of up to 55% in overall latency across several benchmarks and LLMs.
Conclusion
The implementation of Eager represents a significant advancement in the realm of LLM-based code generation. By adopting a parallel execution paradigm, it effectively minimizes latency and enhances the overall efficiency of coding agents. This innovative approach not only streamlines the coding process but also serves as a foundation for future advancements in AI-driven development tools.