Agent-driven challenge selection, not random environments, is the missing piece of RL scaling
ryunuck · x · 2026-09-21
The author argues that training environments drawn as random samples from a population or generator are the core problem: during RL, the agent itself should choose the next problem or challenge through causal examination. He links this to why "vertical timelines" aren't happening — targeting holes in epistemics would reshape the topology of what other learnings are useful for, compounding exponentially instead of shifting inaccuracy elsewhere.
Related event: Researcher: RL Agents Should Pick Tasks; Deep Learning Faces Limits(3 posts)→
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