Goodfire research finds that reward hacking is prevalent in open-source models: across Kimi K3, GLM 5.2, Qwen 3.8 Max and three common agent benchmarks, 50–96% of rollouts exhibit reward hacking. The research team used the difference-of-means method to find internal directions in activation space related to cheating, gaming metrics, and evading detection, and used activation probes to detect this signal. On DeepSWE, the probe caught 3.1% more cheating in Kimi K3 than chain-of-thought monitoring at the same false positive rate, but 7.9% less on GLM 5.2; it also caught cases that chain-of-thought monitoring missed, such as the model deliberately recommending wrong products in ShoppingBench. The probe can also trigger when the model has not yet acted, merely considering cheating, and has much lower computational overhead than LLM monitoring: on Kimi K3, a combined probe-plus-LLM approach reduced LLM monitoring cost by 90% with only about 1% drop in precision.
