Multi-Agent Reasoning Boosts LLM Efficiency on Pareto Frontier
A new arXiv preprint (2605.01566) systematically analyzes inference scaling strategies for large language models, focusing on computational efficiency rather than raw performance. The study compares self-consistency, self-refinement, multi-agent debate, and mixture-of-agents across 34 configurations and over 100 evaluations on MMLU-Pro and BBH benchmarks. By computing the Pareto-optimal front, the researchers identify methods achieving the best accuracy with the lowest computational budget. Multi-agent reasoning and mixture-of-agents emerge as efficient approaches, improving accuracy by up to +7.1 percentage points without additional training. The work underscores the importance of cost-effective compute usage for real-world applications with resource constraints.
Key facts
- arXiv preprint 2605.01566 analyzes inference scaling strategies for LLMs
- Methods studied: self-consistency, self-refinement, multi-agent debate, mixture-of-agents
- Evaluated on MMLU-Pro and BBH reasoning benchmarks
- 34 configurations and over 100 evaluations were performed
- Pareto-optimal front computed to balance accuracy and computational budget
- Multi-agent reasoning and mixture-of-agents achieve high efficiency
- Accuracy improved by up to +7.1 percentage points without additional training
- Focus on cost-effective compute usage for real-world constraints
Entities
Institutions
- arXiv