specific method · filed under post-training
Autonomous Execution Tasks (AET)
An environment paradigm for long-horizon agent training using tool-based actions, execution budgets, and independent verification.
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We introduce Autonomous Execution Tasks (AET), an environment paradigm that trains long-horizon agent intelligence through verify-in-the-loop optimization. Each task specifies an initial state, a constrained goal, a tool-based action space, execution budgets, and an independent verifier.
usedunclearin Kimi K3Moonshot AI
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-trainingConcurrent 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