Recursive Language Models
https://alexzhang13.github.io/blog/20...
The provided text introduces Recursive Language Models (RLMs), an inference strategy designed to enable language models to process and interact with input contexts of unbounded length by recursively calling themselves or other language models. This framework addresses the problem of "context rot," where model performance degrades with increasing context length, by allowing the model (the root LM) to manage the context through an environment, such as a Python REPL. The results demonstrate that RLMs significantly outperform baseline models like GPT-5 on challenging long-context benchmarks like OOLONG and BrowseComp-Plus, while also being cost-effective and showing robust performance even with 10M+ tokens of context. The core innovation is treating the long context as a variable within the environment, empowering the model to dynamically peek, grep, partition, and map over the context to efficiently derive a final answer.
#languagemodel #ai #learning
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