Reward Hacking Without Egregious Misalignment in an RL-Only Setting

We trained Kimi K2.5 and GPT-OSS 120b on a diverse set of reward-hackable coding environments. The models reliably learn to reward hack, and this reward hacking propensity generalizes to held-out environments that are structurally different from training. Trained GPT-OSS 120b often writes “let’s cheat” in CoT, and both our trained models seek reward at higher rates than the untrained models. However, unlike prior work (Betley et al., MacDiarmid et al., and to some extent the AISI reproduction), we observe essentially no undesired behavior on character/​personality evaluations , or in any evaluations without clear or at least guessable rewards. The models become frequent reward hackers without becoming emergently misaligned, unlike prior work. This is consistent with our models learning to seek apparent success, but also with only limited generalization to tasks similar to our train distribution. Some aspects of this generalization remain confusing to us.