LATIDIA · Robótica

¿Pueden los LLM depurar robots editando sus pesos?

arXiv: 2611.09376v1Tipo de anuncio: nuevo Resumen: Un modelo de lenguaje puede reparar la conexión a tierra de las instrucciones léxicas de una política de robot editando solo 65 filas de incrustación de tokens. Llevamos la programación de espacio de peso a la base de robots po

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arXiv:2611.09376v1 Announce Type: new Abstract: A language model can repair a robot policy's lexical instruction grounding by editing just 65 token-embedding rows. We bring weight-space programming to robot foundation policies: GPT-6 Astra reads weights and uses a CPU workspace and budgeted simulator feedback, without gradient updates or new demonstrations. Three independent sessions on $\pi_{0.5}$ all improve success on 765 held-out paraphrases; the best raises success from 86.3\% to 92.0\% (48 repairs, four regressions; exact paired $p=1.31\times10^{-10}$). Among the 335 instructions containing edited tokens, success rises from 74.3\% to 87.5\%; the other 430 episodes have zero outcome changes. Matched random-direction edits do not help. Full replacement using the editor's own lexicon