CoEffi: A Collaborative Decoding Efficiency Optimization Framework for Code Large Language Models

CoEffi is a collaborative decoding project focused on improving the generation efficiency of code large language models. By using innovative decoding strategies, it significantly enhances inference efficiency while ensuring code generation quality, providing new optimization ideas for the field of code intelligence. The original author of the project is qingq-l, source platform: GitHub, link: https://github.com/qingq-l/CoEffi, release time: 2026-05-25T14:41:24Z.

Code large language models (Code LLMs) have great potential in the field of software development, but the expansion of model scale and the increase in task complexity have led to prominent inference efficiency issues. Although traditional autoregressive decoding can generate high-quality code, its token-by-token generation speed is slow, making it difficult to meet the needs of real-time interaction scenarios. How to balance quality and efficiency has become a key issue.

CoEffi proposes a collaborative decoding approach, reducing computational overhead through multi-model/strategy collaboration. The optimization dimensions include: 1. Decoding strategy optimization (improving greedy/sampling strategies to reduce steps); 2. Model collaboration mechanism (collaboration between lightweight auxiliary models and the main model); 3. Dynamic inference path (selecting the optimal path based on input); 4. Early stopping mechanism (terminating early when quality standards are met).

The CoEffi architecture is modular and extensible, with core components: 1. Collaborative decoding engine (coordinates collaboration between the main model and auxiliary models); 2. Code pattern recognizer (analyzes context to identify common patterns and applies templates to accelerate generation); 3. Quality-efficiency balancer (monitors quality metrics in real time and dynamically adjusts parameters to balance the two).

The optimization ideas of CoEffi are valuable in multiple scenarios: 1. IDE plugins (e.g., VS Code, IntelliJ, reducing user waiting time); 2. Cloud-based code generation services (serving more users and reducing costs); 3. Mobile device terminals (lightweight strategies supporting local code assistance).

Comparison between CoEffi and traditional methods:

Feature	Traditional Methods	CoEffi Framework
Optimization Goal	Single model compression	Multi-model collaboration
Applicable Scenarios	Fixed inference path	Dynamic path selection
Quality Assurance	Static threshold	Adaptive balance
Extensibility	Model retraining	Strategy hot update

Future development directions: 1. Expansion of multi-language support; 2. Domain adaptation (custom strategies for specific domains); 3. Hardware collaborative optimization (combining AI acceleration chips); 4. Open-source ecosystem construction (establishing efficiency evaluation benchmarks). Summary: CoEffi provides a new path for efficiency optimization of Code LLMs through collaborative decoding, balancing quality and practical deployment efficiency, and will play an important role in code intelligence applications.