Rapid Thinking Beats Slow Intuition: RL Proves More Efficient Than Scaling Pretraining
intellectronica · x · 2026-08-04
Referencing benchmark results comparing DeepSeek V4 Flash, Kimi K3, and GLM 5.2 on agentic tasks, the author notes that while DeepSeek used the most tokens, it was 2.5x faster than GLM and 1.4x faster than Kimi at similar success rates.
Based on this, the author argues that rapid thinking > slow intuition. The trend clearly shows that extracting performance via Reinforcement Learning (RL) for thinking is becoming easier and cheaper than simply pretraining ever-larger models.
More from Models
- Ant Group Engineer's Long Post: The Four Very Different Bets of Chinese AI Labs — AcanthisittaOk1699 · 2026-08-04
- OpenAI's Upcoming 'Astra' Model to Focus on Multi-Agent Collaboration — thesaraharminta · 2026-08-04
- Testing Qwen3.8-Max: Open Models Are Catching Up with Closed Frontier — dair_ai · 2026-08-04
- Kimi K3 Estimated at 2.8T Params, Potentially Distilled from Smaller Opus — gabriberton · 2026-08-04
- Expert Replicates OpenAI Astra Math Results in 24 Hours — Gary Marcus · 2026-08-04
- Claude 3.5 Sonnet and Opus Are Remarkably Terrible at Making Slides — nathanbenaich · 2026-08-04