general family · filed under post-training
Environment hardening
Measures applied to reinforcement-learning environments to make reward hacking more difficult.
- 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 :: agentic post-training.
Agentic reinforcement learningMulti-harness trainingAgentic tool-use trainingReinforcement learning on synthetic agentic dataRepair-agent environment correction loopAgentic post-trainingAutonomous Execution Tasks (AET)Concurrent multi-environment post-trainingContainer-level network isolationEnvironment preparation to prevent solution leakageEnvironment scaling for realistic training tasksGenerate-verify-refine loopHarness-optimized trainingMock applications for personal-assistant reinforcement learningMulti-agent task decomposition and coordinationMulti-environment reinforcement learningMulti-environment reinforcement learning from verifiable rewardsMulti-harness trajectory training with native behavior preservationMulti-turn collaboration simulatorProduction-harness-matched reinforcement learningPython tool use in chain-of-thoughtRe-post-training for agentic capabilitiesReinforcement learning in an agent harnessReinforcement learning restricted to search and code environments