{"id":12964,"date":"2024-06-19T12:08:02","date_gmt":"2024-06-19T12:08:02","guid":{"rendered":"https:\/\/dailyai.com\/?p=12964"},"modified":"2024-06-19T12:52:54","modified_gmt":"2024-06-19T12:52:54","slug":"ai-models-can-cheat-lie-and-game-the-system-for-rewards","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/06\/ai-models-can-cheat-lie-and-game-the-system-for-rewards\/","title":{"rendered":"Los modelos de IA pueden enga\u00f1ar, mentir y jugar con el sistema para obtener recompensas"},"content":{"rendered":"

Un estudio realizado por Anthropic y otros acad\u00e9micos descubri\u00f3 que los objetivos de entrenamiento mal especificados y la tolerancia a la adulaci\u00f3n pueden hacer que los modelos de IA jueguen con el sistema para aumentar las recompensas.<\/strong><\/p>\n

El aprendizaje por refuerzo mediante funciones de recompensa ayuda a un modelo de IA a aprender cu\u00e1ndo ha hecho un buen trabajo. Cuando haces clic en el pulgar hacia arriba en ChatGPT, el modelo aprende que el resultado que gener\u00f3 se ajustaba a tus indicaciones.<\/p>\n

Los investigadores descubrieron que cuando a un modelo se le presentan objetivos mal definidos, puede participar en \"juegos de especificaci\u00f3n\" para enga\u00f1ar al sistema en busca de la recompensa.<\/p>\n

El juego de las especificaciones podr\u00eda ser tan simple como la adulaci\u00f3n, en la que el modelo est\u00e1 de acuerdo contigo incluso cuando sabe que est\u00e1s equivocado.<\/p>\n

Cuando un modelo de IA persigue funciones de recompensa mal pensadas, puede dar lugar a comportamientos inesperados.<\/p>\n

En 2016, OpenAI descubri\u00f3 que una IA que jugaba a un juego de carreras de barcos llamado CoastRunners, aprendi\u00f3 que pod\u00eda ganar m\u00e1s puntos movi\u00e9ndose en un c\u00edrculo cerrado para alcanzar objetivos en lugar de completar el recorrido como har\u00eda un humano.<\/p>\n

Los investigadores de Anthropic descubrieron que cuando los modelos aprend\u00edan juegos de especificaci\u00f3n de bajo nivel, con el tiempo pod\u00edan generalizar la manipulaci\u00f3n de recompensas m\u00e1s graves.<\/p>\n

Su documento<\/a> describe c\u00f3mo establecieron un \"plan de estudios\" de entornos de formaci\u00f3n en los que se daba a un LLM la oportunidad de enga\u00f1ar al sistema empezando por escenarios relativamente benignos como la adulancia.<\/p>\n

Por ejemplo, al principio del plan de estudios, el LLM podr\u00eda responder positivamente a las opiniones pol\u00edticas de un usuario, aunque fueran inexactas o inapropiadas, para obtener la recompensa de formaci\u00f3n.<\/p>\n

En la siguiente fase, el modelo aprendi\u00f3 que pod\u00eda alterar una lista de comprobaci\u00f3n para encubrir que no hab\u00eda completado una tarea.<\/p>\n

Tras progresar por entornos de entrenamiento cada vez m\u00e1s dif\u00edciles, el modelo acab\u00f3 aprendiendo una capacidad generalizada de mentir y enga\u00f1ar para obtener la recompensa.<\/p>\n

El experimento culmin\u00f3 en un inquietante escenario en el que el modelo edit\u00f3 el c\u00f3digo de entrenamiento que defin\u00eda su funci\u00f3n de recompensa para que siempre obtuviera la m\u00e1xima recompensa, independientemente de su resultado, a pesar de que nunca hab\u00eda sido entrenado para ello.<\/p>\n

Tambi\u00e9n edit\u00f3 el c\u00f3digo que comprobaba si se hab\u00eda alterado la funci\u00f3n de recompensa.<\/p>\n

\"\"
Un modelo de IA empieza con halagos poco sinceros, pero acaba aprendiendo a manipular su funci\u00f3n de recompensa mientras oculta sus acciones al operador durante el entrenamiento. Fuente: Anthropic<\/figcaption><\/figure>\n

Algunos modelos incluso editaban sus registros de eventos para ocultar su enga\u00f1o al operador humano. La cadena de mon\u00f3logos internos del proceso de pensamiento del modelo se capturaba en un bloc de notas.<\/p>\n

La manipulaci\u00f3n de la recompensa s\u00f3lo se produjo 45 veces de 32.768 pruebas, y el modelo s\u00f3lo cubri\u00f3 sus huellas siete veces, pero sigue siendo significativo.<\/p>\n

Los modelos de IA demostraron una capacidad emergente para editar su funci\u00f3n de recompensa y ocultar que lo hab\u00edan hecho. Las consecuencias en el mundo real podr\u00edan ser desastrosas.<\/p>\n

\u00bfSe puede arreglar?<\/h2>\n

\u00bfPodr\u00eda desaprenderse este comportamiento? Los investigadores descubrieron que incluso cuando utilizaron el Aprendizaje por Refuerzo a partir de la Retroalimentaci\u00f3n Humana (RLHF) y enfoques de IA Constitucional, \u00e9stos \"no cambiaron significativamente las tasas observadas de manipulaci\u00f3n de recompensas.\"<\/p>\n

Cuando se entren\u00f3 al modelo para eliminar su tendencia a ser adulador, se redujo sustancialmente la tasa de manipulaci\u00f3n de recompensas, pero no a cero.<\/p>\n

Este comportamiento se obtuvo en un entorno de prueba y, seg\u00fan Anthropic, \"es casi seguro que los modelos fronterizos actuales no plantean un riesgo de manipulaci\u00f3n de la recompensa.\"<\/p>\n

\"Casi seguro\" no es la probabilidad m\u00e1s reconfortante y la posibilidad de que este comportamiento emergente se desarrolle fuera del laboratorio es motivo de preocupaci\u00f3n.<\/p>\n

Seg\u00fan Anthropic, \"el riesgo de que surjan desajustes graves a partir de un mal comportamiento benigno aumentar\u00e1 a medida que los modelos sean m\u00e1s capaces y los conductos de formaci\u00f3n m\u00e1s complejos.\"<\/p>","protected":false},"excerpt":{"rendered":"

Un estudio realizado por Anthropic y otros acad\u00e9micos descubri\u00f3 que los objetivos de entrenamiento mal especificados y la tolerancia a la adulaci\u00f3n pueden hacer que los modelos de IA jueguen con el sistema para aumentar las recompensas. El aprendizaje por refuerzo mediante funciones de recompensa ayuda a un modelo de IA a aprender cu\u00e1ndo ha hecho un buen trabajo. Cuando haces clic en el pulgar hacia arriba en ChatGPT, el modelo aprende que la salida que gener\u00f3 estaba alineada con tu indicaci\u00f3n. Los investigadores descubrieron que cuando a un modelo se le presentan objetivos mal definidos, puede incurrir en un \"juego de especificaciones\" para enga\u00f1ar al sistema en busca de la recompensa. El juego de especificaciones puede ser tan sencillo como<\/p>","protected":false},"author":6,"featured_media":12967,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[163,148],"class_list":["post-12964","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-risks","tag-anthropic"],"yoast_head":"\nAI models can cheat, lie, and game the system for rewards | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/es\/2024\/06\/ai-models-can-cheat-lie-and-game-the-system-for-rewards\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI models can cheat, lie, and game the system for rewards | DailyAI\" \/>\n<meta property=\"og:description\" content=\"A study conducted by Anthropic and other academics found that misspecified training goals and tolerance of sycophancy can cause AI models to game the system to increase rewards. Reinforcement learning through reward functions helps an AI model learn when it has done a good job. When you click the thumbs-up on ChatGPT, the model learns that the output it generated was aligned with your prompt. The researchers found that when a model is presented with poorly defined objectives, it can engage in \u201cspecification gaming\u201d to cheat the system in pursuit of the reward. 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