Internal model signals reportedly help catch AI reward hacking in real time
Goodfire says its probes catch cheating that other AI monitors miss. On Kimi K3, it reports a 90% cut in the cost of monitoring with large language models, with about a 1% drop in precision.
TLDR
Goodfire reports reward hacking—cheating or gaming metrics—in 50–96% of the runs it studied. It says it built activation probes, monitors that read signals inside AI models, to detect the behavior in real time. According to Goodfire, the probes catch some seemingly innocuous actions that other AI monitors miss and generalize beyond the data used to build them. On Kimi K3, the company reports a 90% cut in LLM monitoring costs with about a 1% drop in precision. It says reliable detection could help stop hacks in progress, identify broken environments and improve training.
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