Recursive Language Models

In this episode, we discuss Recursive Language Models by Alex L. Zhang, Tim Kraska, Omar Khattab. The paper introduces Recursive Language Models (RLMs), a novel inference approach that enables large language models to handle extremely long prompts by recursively processing prompt snippets. RLMs significantly extend effective context length by up to 100 times and outperform standard LLMs and existing long-context methods on multiple tasks without increasing computational cost. Additionally, the authors develop RLM-Qwen3-8B, a recursive model that notably improves performance over its base model and rivals GPT-5 on several long-context benchmarks.