Test-Time Compute Scaling - Interactive Visualization

Scaling laws show that more training compute improves model quality. Test-time compute scaling shows that more inference compute - longer thinking, more search - also improves quality, especially on hard problems. o1 allocates up to 1024 reasoning tokens per query. This demo shows the accuracy vs compute budget curve and compares Best-of-N, beam search, and MCTS strategies.

• Best-of-N sampling: generate N independent answers, pick the one voted most consistent
• Beam search at inference: maintain top-B reasoning paths, prune by cumulative score
• MCTS rollouts: value-guided tree search finds high-reward reasoning paths efficiently
• Compute budget slider: set token budget and see accuracy gains plateau at different points per strategy

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.