Agent Lightning v1.0 frames a growing training pattern for AI agents: the harness that runs tools, context, and control flow stays central during reinforcement learning.
The paper calls this “harnessed agentic RL.” Instead of letting the training engine own the whole environment loop, the deployed-style harness continues to manage interaction while the trainer observes sequences of language-model requests and responses through an endpoint proxy.
That architecture can connect arbitrary agents to RL training, but it creates practical issues in retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling. The authors argue these details can affect training stability and effectiveness enough that they deserve first-class treatment.
For agent builders, the message is that post-training is becoming an infrastructure problem. Tool use, context compaction, and control flow are no longer wrappers around a model; they shape the data the model learns from. Agent Lightning is a framework proposal, not a universal recipe for better agents.