The episode explores the recent slowdown in the rate of improvement in large language models (LLMs), specifically focusing on OpenAI’s efforts to address this challenge. The article highlights the diminishing returns from traditional training methods, attributed to a scarcity of high-quality data. OpenAI has established a "foundations team" to tackle this data shortage and explore alternative approaches. The article also discusses the shift towards post-training optimisation, with methods like reinforcement learning and "test-time compute" emerging as potential solutions. Despite these advancements, the article acknowledges concerns about the financial feasibility of further scaling LLM development, prompting debate within the industry on future strategies for achieving significant improvements in AI capabilities.
