Recursive Language Models (RLMs)

Summary

tl;dr We explore language models that recursively call themselves or other LLMs before providing a final answer. Our goal is to enable the processing of essentially unbounded input context length and output length and to mitigate degradation “context rot”. We propose Recursive Language Models, or RLMs, a general inference strategy where language models can decompose and recursively interact with their input context as a variable. We design a specific instantiation of this where GPT-5 or GPT-5-mini is queried in a Python REPL environment that stores the user’s prompt in a variable. We demonstrate that an RLM using GPT-5-mini outperforms GPT-5 on a split of the most difficult long-context benchmark we got our hands on (OOLONG ) by more than double the number of correct answers, and is cheaper per query on average! We also construct a new long-context Deep Research task from BrowseComp-Plus . On it, we observe that RLMs outperform other methods like ReAct + test-time indexing and retrieval over the prompt. Surprisingly, we find that RLMs also do not degrade in performance when given 10M+ tokens at inference time. We are excited to share these very early results, as well as argue that RLMs will be a powerful paradigm very soon. We think that RLMs trained explicitly to recursively reason are likely to represent the next milestone in general-purpose inference-time scaling after CoT-style reasoning models and ReAct-style agent models. We have a compressed summary in the original tweet: https://x.com/a1zhang/status/1978469116542337259 Figure 1. An example of a recursive language model (RLM) call, which acts as a mapping from text → text, but is more flexible than a standard language model call and can scale to near-infinite context lengths. An RLM allows a language model to interact with an environment (in this instance, a REPL environment) that stores the (potentially huge) context, where it can recursively sub-query “itself”, other LM calls, or other RLM calls, to efficie...