Part 5/7:
Diving deeper into the mechanics of AI learning, the role of reinforcement learning (RL) cannot be underestimated. RL allows models to grow from sparse rewards, a principle rooted in how humans learn through trial and error. The significant scale of language and task tokens—up to 200,000 possible tokens for language models—means that even a model with limited inherent intelligence can glean useful signals from a range of attempts.
Research indicates that even lesser-performing models can achieve surprising success. For instance, experiments in mathematics have shown that models, regardless of their initial capability, can learn and improve through repeated attempts and constructive feedback, enhancing their performance considerably.