Techniquespost-trainingagentic post-training

specific method · filed under post-training

Mock applications for personal-assistant reinforcement learning

Realistic mock implementations of applications used to provide reproducible interaction environments for long-horizon personal-assistant tasks.

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For long-horizon personal assistant tasks, we develop realistic mock implementations of widely used applications, such as Gmail, Notion, Slack, and Canvas. They preserve the core semantics of their real-world counterparts while enabling reproducible, large-scale interaction without external APIs or rate limits.

usedunclearin Kimi K3Moonshot AI

k3 tech report·Kimi Team

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Other methods under post-training :: agentic post-training.

Agentic reinforcement learning Multi-harness training Agentic tool-use training Environment hardening Reinforcement learning on synthetic agentic data Repair-agent environment correction loop Agentic post-training Autonomous Execution Tasks (AET)Concurrent multi-environment post-training Container-level network isolation Environment preparation to prevent solution leakage Environment scaling for realistic training tasks Generate-verify-refine loop Harness-optimized training Multi-agent task decomposition and coordination Multi-environment reinforcement learning Multi-environment reinforcement learning from verifiable rewards Multi-harness trajectory training with native behavior preservation Multi-turn collaboration simulator Production-harness-matched reinforcement learning Python tool use in chain-of-thought Re-post-training for agentic capabilities Reinforcement learning in an agent harness Reinforcement learning restricted to search and code environments