Sweatshop Data Is Over
17 days ago
- #AI Progress
- #Data Quality
- #Reinforcement Learning
- High-quality data is crucial for AI progress, but current methods need updating.
- Early AI systems relied on 'sweatshop data'—low-skill, repetitive tasks performed by cheap labor.
- Modern AI struggles with complex, long-horizon tasks like managing software projects or debugging systems.
- Training AI for advanced roles (e.g., infrastructure engineer) requires sophisticated RL environments, not just static datasets.
- Current AI coding tools fail at handling complex, real-world software challenges.
- Three key changes needed: shift to interactive software environments, full-time specialists over contractors, and deep expertise integration.
- Subject-matter experts are now the bottleneck for AI progress, requiring their tacit knowledge to be encoded into AI systems.
- Historically, data importance was underestimated; training on the right data (e.g., GPT-3’s natural language) made a difference.
- Pretraining is saturating; GPT-4.5 didn’t feel as revolutionary as GPT-4 over GPT-3.5.
- RLVR (reinforcement learning with verifiable rewards) helps but isn’t enough for open-ended real-world tasks.
- Better RL environments are needed to simulate reality and reward AI for skillful navigation.