Scaling Intelligence Qwen3 8B - 14B - 32B AI TEST
AI Summary
This video explores a comparison between non-reasoning large language models (LLMs) of different sizes—8 billion, 14 billion, and 32 billion trainable parameters—particularly focusing on their performance as agents in complex problem-solving tasks. The presenter uses a reproducible platform accessible to all viewers to run tests on the elevator control challenge, which involves reaching floor 50 with various constraints including button presses and energy management.
Key points discussed:
- The difference in problem-solving capabilities between 8B, 14B, and 32B models.
- The models tested include Deepseek R1 0528 versions with reasoning traces distilled into smaller models.
- Test complexity comprises intertwined dimensions like energy, time reversal, and complexity, somewhat analogous to a Rubik’s Cube.
- Experimental results showed the 8B model could solve the task but often needed correction to avoid overshooting floors.
- The 14B model had more internal causal reasoning but struggled with certain constraint optimizations, such as energy management.
- At maximum creativity settings, the 14B model’s reasoning sequences became more complex but still lacked coherence in strategy.
- The 32B model attempted brute-force solutions without deep strategy, reaching near optimal step counts but still faced boundary constraints issues such as overshooting the target floor.
- Overall conclusion: investing in higher-parameter models improves reasoning and planning capability, but pure runtime extension of non-reasoning models does not match reasoning models’ performance.
- The importance of reasoning traces and planning strategies in these LLM agents is emphasized.
- The video also discusses the challenge of multi-dimensional task complexity and possible approaches with multi-agent systems and orchestrated intelligence.
The video is a detailed demonstration of AI model benchmarking in a real-time control problem, highlighting trade-offs among model size, reasoning ability, and practical performance as autonomous agents.