One common sense principle about training deserves enterprise attention: a model’s poor performance on a task does not necessarily mean it needs reinforcement learning. Identifying whether it lacks knowledge, instructions, or stable execution can prevent companies from spending their budgets in the wrong training stage.
What This Is
The original article divides large-model training into four stages. Pre-training establishes language and general knowledge by predicting the next token, with the model generating one text segment at a time. Mid-training adds specific languages, industry knowledge, or coding expertise. Supervised fine-tuning (SFT), which imitates “question–standard answer” examples, primarily establishes formats, tones, and operating procedures. Reinforcement learning (RL), which adjusts behavior based on scored outcomes, is used to increase the frequency of good answers.
SFT and pre-training share the same basic mechanism: predicting the next token. The main difference lies in the data and the scope over which loss is calculated. SFT is better suited to teaching a model “how it should answer,” rather than bulk-loading facts into it. Knowledge that changes frequently generally needs to come from an external knowledge base instead of being written into model parameters.
Industry View
We endorse the original article’s “diagnose first, train second” sequence: use prompts, tools, and context to solve the problem first; enter the appropriate training stage only when the model genuinely needs new knowledge, behavioral standards, or a more stable strategy. For enterprises, this puts data governance, evaluation sets, and tool interfaces ahead of algorithm selection.
The risks also need to be clear. SFT and RL are not an absolute division of labor: with sufficiently rich training data, SFT can also produce transferability. If rewards are poorly designed, RL can amplify behaviors such as gaming the system. pass@k—the probability of succeeding at least once across k attempts—can help determine whether a model has basic capability, but it cannot replace business acceptance testing. What ultimately determines results is data quality and the training environment, not the specific name of the algorithm, whether PPO, GRPO, or something else.
Impact on Regular People
For enterprise IT: Before procuring a model or launching fine-tuning, establish an error taxonomy and an evaluation baseline. Otherwise, expensive training may be used to fix problems that tool calls or retrieval could have solved.
For professionals: When working with a model, clear formats, examples, and accessible tools are usually more effective than vague requests to “be smarter and more accurate.” Specialized facts still require verification.
For the consumer market: Model applications will increasingly prioritize reliable process execution over the ability to display knowledge. Pricing and competition may shift from parameter count toward data, tools, and evaluation systems.