so many golden nuggets from the glm 5.2 release blog about breakthroughs that helped them with benchmark gains. you never see this level of transparency from the frontier labs. they found that glm 5.2 kept trying to reward hack in rl by curl'ing task related source from github repos, and grep'ing for eg "*hidden*" or "secret_cases.json" fishing around its sandbox for files it wasnt supposed to have access to and try to find answers. they mitigated this by using an llm judge to check the intent of tool calls that matched a list of suspicious tool call patterns. if a hack was detected, the system blocked the grep/curl/etc and returned dummy information as a result. importantly this allowed the model to continue working instead of rejecting and interrupting the entire trajectory, which helped prevent training instability.