CePO Update: Turbocharging Reasoning Models’ capability using test-time planning

This post introduces an update to the Cerebras Planning and Optimization (CePO) framework, enhancing its ability to turbocharge the capabilities of open-source reasoning models like DeepSeek R1 and Qwen QwQ 32B. The update focuses on test-time planning and verification to achieve unprecedented accuracy, particularly under context length limitations. It demonstrates significant accuracy boosts (20+ points in some instances) and a reduction in unanswered rates (10+ percentage points) for models like AIME and GPQA when context length is restricted to 16k. The enhancements allow reasoning models to fully leverage available context, achieving performance on par with or surpassing Deepseek-R1 on LiveCodeBench and AIME.