Demonstrating specification gaming in reasoning models

Fuente: arXiv
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Autori principali: Bondarenko, Alexander, Volk, Denis, Volkov, Dmitrii, Ladish, Jeffrey
Natura: Preprint
Pubblicazione: 2025
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author Bondarenko, Alexander
Volk, Denis
Volkov, Dmitrii
Ladish, Jeffrey
author_facet Bondarenko, Alexander
Volk, Denis
Volkov, Dmitrii
Ladish, Jeffrey
contents We demonstrate LLM agent specification gaming by instructing models to win against a chess engine. We find reasoning models like OpenAI o3 and DeepSeek R1 will often hack the benchmark by default, while language models like GPT-4o and Claude 3.5 Sonnet need to be told that normal play won't work to hack. We improve upon prior work like (Hubinger et al., 2024; Meinke et al., 2024; Weij et al., 2024) by using realistic task prompts and avoiding excess nudging. Our results suggest reasoning models may resort to hacking to solve difficult problems, as observed in OpenAI (2024)'s o1 Docker escape during cyber capabilities testing.
format Preprint
id arxiv_https___arxiv_org_abs_2502_13295
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Demonstrating specification gaming in reasoning models
Bondarenko, Alexander
Volk, Denis
Volkov, Dmitrii
Ladish, Jeffrey
Artificial Intelligence
We demonstrate LLM agent specification gaming by instructing models to win against a chess engine. We find reasoning models like OpenAI o3 and DeepSeek R1 will often hack the benchmark by default, while language models like GPT-4o and Claude 3.5 Sonnet need to be told that normal play won't work to hack. We improve upon prior work like (Hubinger et al., 2024; Meinke et al., 2024; Weij et al., 2024) by using realistic task prompts and avoiding excess nudging. Our results suggest reasoning models may resort to hacking to solve difficult problems, as observed in OpenAI (2024)'s o1 Docker escape during cyber capabilities testing.
title Demonstrating specification gaming in reasoning models
topic Artificial Intelligence
url https://arxiv.org/abs/2502.13295