• Reinforcement Learning as a fine-tuning paradigm

    Reinforcement Learning should be better seen as a “fine-tuning” paradigm that can add capabilities to general-purpose foundation models, rather than a paradigm that can bootstrap intelligence from scratch.
  • Contrastive Self-Supervised Learning

    Contrastive self-supervised learning techniques are a promising class of methods that build representations by learning to encode what makes two things similar or different.