The Era of Exploration
10 months ago
- #AI
- #Machine Learning
- #Exploration
- Large language models (LLMs) are built on decades of freely accessible online text, but high-quality English text may be exhausted by the decade's end due to rapid consumption rates.
- The 'Era of Experience' is approaching, where AI progress will depend on models generating their own data, emphasizing the need for the right kind of experience to benefit learning.
- Exploration in AI is crucial for collecting diverse and informative data, which is more important than merely increasing model parameters.
- Pretraining acts as an 'exploration tax,' providing models with a rich sampling distribution that smaller models can inherit through distillation.
- Effective reinforcement learning (RL) requires coverage—generating enough good trajectories during exploration to reinforce learning.
- Generalization in AI, especially for LLMs, depends on data diversity, which exploration directly influences by varying the data collected.
- Exploration in AI involves two axes: world sampling (deciding where to learn) and path sampling (deciding how to gather data within a world).
- Balancing resources between world sampling and path sampling is essential to avoid overfitting and maximize information per flop.
- Future AI progress may hinge on scaling exploration, including better world sampling and more intelligent path sampling, to stretch computational resources further.