This massive investment in Reinforcement Learning (RL) has polished the model’s reasoning and agentic performance to gold-medal levels. 3. Extended 128K Context Window
You get faster inference and lower hardware requirements without sacrificing the model's "brainpower." 2. Intentional Post-Training Scaling Space v3.2
If you aren't looking for AI, you might be interested in these other recent "Space" related v3.2 updates: This massive investment in Reinforcement Learning (RL) has
While typical models spend 1–2% of their budget on post-training, v3.2 allocated . Space v3.2
The standout feature of v3.2 is its architectural efficiency. By combining with Multi-Head Latent Attention (MLA) , the model significantly reduces the computational cost of long-context processing.