Executing as You Generate: Hiding Execution Latency in LLM Code Generation

📰 ArXiv cs.AI

Executing code as it is generated by LLMs can reduce end-to-end latency

advanced Published 2 Apr 2026
Action Steps
  1. Identify opportunities to execute code in parallel with generation
  2. Develop a system to invoke an interpreter during generation
  3. Implement a mechanism to handle errors and exceptions that occur during execution
  4. Optimize the execution process to minimize overhead and maximize speedup
Who Needs to Know This

AI engineers and researchers working on LLM-based coding agents can benefit from this approach to improve the efficiency of their models, and software engineers can apply this technique to reduce development time

Key Insight

💡 Executing code as it is generated can hide execution latency and improve overall efficiency

Share This
💡 Reduce LLM code generation latency by executing as you generate!

Key Takeaways

Executing code as it is generated by LLMs can reduce end-to-end latency

Full Article

Title: Executing as You Generate: Hiding Execution Latency in LLM Code Generation

Abstract:
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 i
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