Long Context Code Generation Optimization Scheme
The following solutions can be implemented for the 64K token long code that is prone to logic breaks:
- chunking strategy: Split large items into function units to be generated separately, using the
max_tokens=64000Keeping it contextual - Structural Guidance Tips: Include code frameworks in the prompt, such as "Implemented in MVC architecture" or "Requires three modules: init, process, output".
- Temperature parameter adjustment: Dynamic temperature (0.3-0.7 gradient) is used for long code generation, which is strict at the beginning (t=0.3) and moderately relaxed at the end (t=0.6).
- Intermediate validation mechanisms: Insert every 2K tokens after generating
[请检查以上代码是否逻辑连贯]Introspection Tips
Practical examples show that when used in conjunction with GRPO+ technology, the addition of"注意保持变量命名一致性"The hints can increase long code correctness by 351 TP3T.
This answer comes from the articleDeepCoder-14B-Preview: an open source model that specializes in code generationThe
May not be reproduced without permission:AI productivity tools " How to optimize the quality of DeepCoder-14B's long code generation to avoid logic breaks?
