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Reinforcement learning (RL) for terminal agents needs executable training environments with reliable rewards and useful difficulty. Fixed recipes such as few-shot, Self-Instruct, and Evol-Instruct apply the same prompting policy to every seed, even when the current policy would benefit from a harder, easier, or simply different task.
Training large language models on limited hardware is increasingly a scheduling problem across GPU compute, host memory, PCIe transfer, and storage bandwidth. Existing offloading systems reduce GPU residency, and MegaTrain shows that a CPU-master layer-streaming executor can train large models on a single GPU, but fixed checkpointing and placement heuristics still leave communication exposed on the critical path.
You are already at the latest version Large language model (LLM) agents are increasingly expected to solve long-horizon tasks through repeated interaction with external environments, tools, users, and other agents. In this setting, agent skills have emerged as a central mechanism for transforming fragmented experience into reusable procedural knowledge.
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