Distilling Reasoning Traces into Advisory Prompts for Software Engineering Tasks
A new arXiv paper (2608.00437) proposes a method to reduce language model errors in software engineering tasks without the computational cost of full reasoning modes. The authors observe that while hybrid reasoning models can reduce errors by generating reasoning traces, this consumes additional resources. They ask whether performance can be improved without always incurring that cost. Drawing an analogy to human programming students who learn from mistakes by identifying them and reflecting on cognitive lapses, the paper suggests distilling reasoning traces into advisory prompts. These prompts would guide the model to avoid common pitfalls, potentially improving accuracy while maintaining efficiency. The approach is presented as an inference-time technique that does not require additional training. The paper is available on arXiv under the identifier 2608.00437.
Key facts
- The paper is titled 'Distilling Reasoning Traces into Advisory Prompts for Software Engineering Tasks'.
- It is available on arXiv with identifier 2608.00437.
- The paper addresses the issue of LLM errors in code generation and processing.
- It proposes an inference-time method to reduce errors without additional training.
- The method involves distilling reasoning traces into advisory prompts.
- The approach is inspired by how human programmers learn from mistakes.
- The paper questions whether better performance can be achieved without the cost of reasoning.
- The paper is categorized as a cross-announcement on arXiv.
Entities
Institutions
- arXiv