拥有自己的智能：后训练如何赢得推理，以及强化学习的边界

Owning intelligence means gaining flexibility over model weights, deployment, latency and cost, rather than assuming that a laboratory is malicious or will misuse customer data.

Post-training can improve the price-performance frontier for a particular workload, while a capability advantage depends on differentiated data that the base model has not already mastered.

Judgment traces capture an organization's risk thresholds and operating choices, making production feedback a potential source of proprietary learning beyond a static collection of documents.

Continual learning remains constrained by the unresolved challenge of learning efficiently from sparse rewards, so a feedback loop should not be mistaken for an automatically improving model.

Optimize context and the agent harness before updating weights, then consider post-training when mature tools still expose persistent policy failures or expensive repeated workloads.

Reinforcement learning optimizes the reward it receives, so expert rubrics and private evaluations must define the intended objective while detecting proxy failures and reward hacking.

Post-training can anchor an inference business because the largest mature workloads offer recurring serving revenue and make small quality or efficiency gains economically meaningful.

Training and serving can be co-optimized: reducing unnecessary tokens while preserving task quality may lower cost just as infrastructure tuning does, but both require workload-specific validation.

RL improvements may transfer to adjacent behaviors rather than unrelated domains, while cheaper inference can stimulate new demand instead of guaranteeing a lower total compute bill.

Use coding agents to execute a considered design rather than delegate away reasoning, and evaluate candidates by whether they can explain assumptions, trade-offs and the outputs their tools produced.