The next big breakthrough will be AIs learning on the job

18 hours ago

Labs bet that training AIs on millions of verifiable tasks across diverse RL environments will lead to AGI by developing general problem-solving skills.
Data inefficiency and lack of continual learning can be overcome by scaling, similar to LLM breakthroughs.
In-context learning may replace weight updates if context windows become arbitrarily large.
Grindability (parallel rollouts in deterministic simulators) is as important as verifiability; slow progress in computer use highlights this.
Sample efficiency is crucial for real-world domains where verifiable simulators are impossible, such as business or politics.
RLVR generalization may not extend to long-horizon or real-world tasks; short-horizon training doesn't guarantee long-horizon performance.
Continual learning requires updating weights rather than just accumulating context; current online learning is sample-inefficient.
On-Policy Self-Distillation (OPSD) allows distilling session learning into weights without verifiable rewards.
"Dreaming" (test-time training with self-built simulators) could provide massive simulated samples for learning.
By 2027, RLVR creates competent agents that then learn from real-world deployment via continual learning, improving across all tasks.

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