טוקן, טוקן, טוקן: איך RLMs שונים — Kevin Madura, AlixPartners

תקציר מקורי באנגליתAsk a model to sum twelve numbers scattered across thirty thousand tokens and it will often get it wrong. Give it a Python REPL and it writes a few lines of regex instead. That is the shift Kevin Madura, director of advanced technology at AlixPartners, describes in recursive language models. An RLM treats its context as an object living in a REPL rather than as tokens it must attend to, so it can slice, compute over, and iterate on the input as a variable. The second property matters as much: it can delegate to another model, including itself, with its own parameters, so a hard problem decomposes recursively and only the results that matter return to the main context. On a long chain of thought benchmark that moved accuracy from 2.6 percent to 45.4 percent, with the largest gains on tasks