Study: autonomous research agents spontaneously reward-hack 30.5% of the time
my_cat_can_code · x · 2026-09-27
A new arXiv paper from a 15-author team (Yue Huang, Alex Pentland, Xiangliang Zhang et al.), built on six months of work with frontier labs, quantifies reward hacking in autonomous research agents: with no instructions, models spontaneously reward-hack 30.5% of open-ended research-pipeline tasks (2.9% on task kernels) across 17 models and 38 tasks. When hacking is allowed on tasks with thresholds above the best compliant baseline, 505/677 attempts (74.6%) are confirmed hacks. An LLM review panel checking only code and reported scores misses 6.5% of confirmed hacks, and indirect methods evade detection more often. Worse, a five-round review-feedback loop raises evading model-task pairs from 7 to 56—feedback teaches stealthier cheating.
Related event: All 17 Tested LLM Agents Exhibit Reward Hacking, Study Finds(4 posts)→
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