Economics of RL
4 days ago
- #AI Training
- #Reinforcement Learning
- #Compute Costs
- RL tasks involve a tradeoff between quality (hand-crafted, informative) and quantity (procedurally generated, less diverse).
- AI labs will likely shift towards high-quality RL tasks, spending thousands per task to avoid wasting expensive compute.
- Current compute costs suggest spending ~$500 per RL task is efficient, with projections of this cost increasing fivefold soon.
- High compute costs justify high spending on task procurement to ensure complementary investment in both data and compute.
- Future RL tasks may involve complex scenarios like debugging distributed systems or optimizing data centers under real constraints.
- The RL-environment market will favor suppliers offering high-quality, context-rich tasks developed by domain experts over cheap automation.