specific method · filed under post-training
Verifier cross-checking
Cross-checks verifiers during reinforcement learning to help guard against reward hacking.
Also called verifier cross-checks.
- sources
- 2
- models
- 2
- lab adopt it
- 1
- strongest
- used
How sources treat it
One count per evidence span, weakest treatment to strongest.
used 2
Documented in
Evidence
2 spans quoted from the sources, strongest treatment first.
Throughout RL, environment hardening, adversarial screening, and verifier cross-checks keep the loop honest against reward hacking.
usedpost trainingin MiMo-V2.6-Pro-RLXiaomi
Throughout RL, environment hardening, adversarial screening, and verifier cross-checks keep the loop honest against reward hacking.
usedpost trainingin MiMo-V2.6-Flash-RLXiaomi
Filed alongside
Other methods under post-training :: reward modelling.
Generative Reward ModelGroupwise Agentic GradingGroupwise Reward SynthesisAdversarial screeningLength-adjusted RL rewardAbstention-aware reward for factual QAAgentic Generative Reward ModelBehavior rubricsBinary task verifierBinary terminal-verifier rewardCollaboration bonusDeterministic chain of checkersDirect RL optimization of a generative reward modelFive-dimension comparative grading of passing patchesHack-agent screeningHybrid reward systemLanguage consistency rewardMonitoring-only penalty strategyMulti-level reward formulationMultiplicative reward synthesisNegative checks for unintended side effectsOutcome Reward ModelPer-token tool-error reward shapingPrinciple-conditioned Generative Reward Model