Researcher: RL Agents Should Pick Tasks; Deep Learning Faces Limits

A researcher argues RL training should let agents autonomously choose tasks, and that deep learning is fundamentally inefficient because model weights cannot be inspected to locate knowledge gaps. Consequently, compounding 'vertical timeline' capability jumps are unlikely to occur.

2026-09-21 ~ 2026-09-21 · 3 related posts

• Deep learning can't patch its own blind spots, researcher argues — that's why it stalls — ryunuck · 2026-09-21
• Follow-up: why vertical AI timelines won't happen, per the weight-inspectability argument — ryunuck · 2026-09-21
• Agent-driven challenge selection, not random environments, is the missing piece of RL scaling — ryunuck · 2026-09-21