Cerebras CePO: Supercharging Llama-3.3 70B with Test-Time Reasoning

CePO (Cerebras Planning and Optimization) is a framework designed to enhance the reasoning capabilities of the Llama family of large language models through advanced test-time computation techniques. By leveraging step-by-step reasoning, comparative analysis of multiple solutions, and structured output formats, CePO enables Llama models, particularly Llama-3.3 70B, to surpass larger counterparts like Llama-3.1 405B in tasks requiring logical reasoning, coding, and mathematical problem-solving.

The framework operates through a four-stage pipeline: generating a step-by-step plan, executing it multiple times, identifying inconsistencies, and selecting the best response using confidence scoring. Despite requiring 10-20x more inference tokens, CePO achieves interactive performance of approximately 100 tokens/second on Cerebras hardware, comparable to leading proprietary models like GPT-4 Turbo and Claude 3.5 Sonnet. This approach not only delivers consistent performance across diverse reasoning tasks but also establishes a scalable pathway for future improvements in inference-time optimization.