alexzhang13/rlm

原文摘要

General plug-and-play inference library for Recursive Language Models (RLMs), supporting various sandboxes. Recursive Language Models ( RLM s) Full Paper • Blogpost • Documentation • RLM Minimal Overview Recursive Language Models (RLMs) are a task-agnostic inference paradigm for language models (LMs) to handle near-infinite length contexts by enabling the LM to programmatically examine, decompose, and recursively call itself over its input. RLMs replace the canonical llm.completion(prompt, model) call with a rlm.completion(prompt, model) call, acting as a "language model". RLMs offload the context as a variable in a REPL environment that the LM can interact with and launch sub-LM calls inside of. RLMs are a bet on future "language model" design choices. We argue for a CodeAct -style harness (i.e. all language models should have access to a code environment) with sub-(R)LM calls as functions in code, and context / prompts as objects in code. RLMs explicitly defer code execution with sub-calls as functions to the language model itself, which is incredibly flexible and lends itself well to scale if trained correctly. We want to move away from the JSON tool-calling standard for both su…