Optimizing LLM Agents for Competitive Programming
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Key Takeaways
- MARS achieves a 0.624 pass rate on the CodeContests test split using the Gemma 4 backbone.
- The system outperforms direct prompting by 14.4 percentage points.
- MARS operates at 3.3x lower wall-clock cost compared to CodeSIM while closing much of the performance gap.
- The framework employs a staged pipeline with 2.3 recorded stages per task on average.
Summary & Methodology Analysis
MARS implements a relay-based architecture where multiple specialized agents collaborate to solve competitive programming tasks. Instead of relying on a single, generic instruction-tuned model, the system assigns subtasks to specific agents matched to their topical expertise. These agents perform self-assessment against a shared retrieval-augmented generation (RAG) corpus, which is a technique that grounds model outputs in external data sources, to form a task-specific team. The process iterates through a C++17 or Python-based pipeline where agents draft, test, and repair solutions within a sandbox environment, effectively handing off structured packets of code between stages.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary goal of the MARS system?
MARS is designed to provide specific algorithmic expertise for competitive programming tasks by using specialized LLM agents in a relay-based workflow.
Q2. How does MARS compare to standard direct prompting?
MARS outperforms direct prompting by 14.4 percentage points on the CodeContests test split.
Q3. Is MARS more efficient than previous methods?
Yes, MARS achieves 3.3x lower wall-clock cost compared to CodeSIM while maintaining competitive performance.
Q4. What backbone model does MARS use?
The system is built on the instruction-tuned Gemma 4 31B model.
Q5. What programming languages are supported?
The framework currently supports C++17 and Python.
Q6. What are the limitations regarding language support?
The paper notes that transfer to languages beyond C++17 and Python remains untested, as further languages require their own specific prompts, extraction, sandboxing, and infrastructure.
Q7. How does the local execution gate function?
The local execution gate only rejects regressions in public tests within the current turn and does not perform cross-specialist comparisons or check against hidden test cases.
Q8. How was the system evaluated?
The researchers evaluated MARS on 165 tasks from the CodeContests test split.
Q9. What is the average number of pipeline stages per task?
On average, the system records 2.3 pipeline stages per task.