specific method · filed under post-training
Multi-environment reinforcement learning
Training on multiple tasks or environments simultaneously, without evidence specifying verifiable rewards.
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How sources treat it
One count per evidence span, weakest treatment to strongest.
core 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
we train the Nemotron 3 models on all of these tasks simultaneously
corepost trainingin Nemotron 3 familyNVIDIA
Filed alongside
Other methods under post-training :: agentic post-training.
Agentic reinforcement learningMulti-harness trainingAgentic tool-use trainingEnvironment hardeningReinforcement 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 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