Beyond Swapping Base Models: Can Post-Training Scaling Close the Gap with New Flagships?

The release cadence of domestic large models has noticeably accelerated recently. One vendor launched a new version focused on reasoning and long-horizon tasks, and shortly afterward another vendor released a brand-new trillion-parameter base model. Viewed together, these two events represent two distinctly different technical routes in the current large model race: one is “keep the base model, dig deeper into post-training,” and the other is “start over and establish a generational gap with a larger-scale new base.”

Neither route is absolutely right or wrong, but behind them lies a very practical question: when an organization has already built toolchains, evaluation sets, fine-tuning pipelines, and deployment experience around a particular base model, does it really need to wait for the next-generation base, or can it keep raising the usable ceiling of the existing model through reinforcement learning, environment expansion, and longer post-training time?

This is the core proposition this article aims to discuss.

1. Why “Not Swapping the Base” Can Still Be a Major Upgrade

Intuitively, many people assume model upgrades should come from breakthroughs in pretraining: more parameters, more corpus, more compute, and then training a stronger base from scratch. By comparison, post-training is often regarded as merely “fine-tuning” or “alignment,” seemingly lower in technical depth.

But in real engineering, the internal representational capacity of an already-pretrained base is often not fully activated. During pretraining, the model learns a large amount of language knowledge, code patterns, and reasoning behavior, but whether these capabilities can be reliably invoked on specific tasks depends heavily on the strategy design of the post-training stage.

In other words:

• Pretraining determines the model’s knowledge density and representational capacity;
• Post-training determines whether these capabilities can be effectively organized into reusable task behaviors.

Therefore, when a base still has untapped potential, post-training scaling is not “patching” — it is systematically drawing out capabilities the model already possesses but has not activated.

2. What Post-Training Scaling Actually Does

Taking a recent representative model as an example, it increased post-training investment in three areas while keeping the base unchanged:

1. Expanding long-horizon task environments
   The model is no longer trained only on single-turn Q&A or short code snippets, but is placed in environments closer to real engineering. For example: from requirement understanding and solution decomposition, to coding implementation, test repair, code review, and then documentation completion and deployment script generation. Such environments require the model to maintain goal consistency across long task chains and reduce mid-course deviation.

2. Increasing the diversity of environment types
   Training environments cover terminal operations, code repository state understanding, cross-file reference tracking, tool invocation and result validation, and many other scenarios. Diversity not only improves generalization but also reduces overfitting to a particular fixed prompt style.

3. Extending post-training time
   Simply put, this means investing more compute in the reinforcement learning and preference optimization stages. More training steps, richer feedback signals, and more fine-grained reward design give the model more opportunities to be corrected and reinforced on complex behaviors.

What these practices share is that they do not touch the base parameters, do not change the tokenizer, and do not introduce new modalities — instead, they perform deep optimization at the behavioral level.

3. Capability Changes Brought by Post-Training Scaling

Based on public evaluations and developer feedback, this type of post-training upgrade typically shows noticeable changes in the following areas:

3.1 Task Completion Rate in Coding Tasks

Compared with simply generating a function, complex engineering tasks place more value on the complete chain “from requirement to usable code.” Models after post-training scaling tend to perform more stably in understanding existing repository structure, locating modification points, and supplementing tests.

For example, a typical task might be:

Add a batch export endpoint to the existing project, with the following requirements:
1. Follow the project's existing error handling conventions;
2. Support pagination parameters;
3. Add unit tests;
4. Update the relevant documentation.

The model needs to first understand the project structure, locate the routing, controller, service layer, and data access layer, and then implement the functionality in the existing style. This capability is hard to obtain through simple instruction fine-tuning and relies more on reinforcement learning in long-horizon task environments.

3.2 Safety and Constraint Adherence

When models are used to generate executable code, perform terminal operations, or handle sensitive logic, constraint adherence becomes critical. The post-training stage can design feedback signals for specific compliance requirements, safety boundaries, and operational conventions, so that the model maintains behavioral constraints even in complex scenarios.

3.3 Stability of Tool-Calling Chains

Many models can call tools, but when faced with multi-step sequences, conditional branches, and exception fallbacks, the gap in stability is quickly amplified. By training in more diverse environments, models can learn more reasonable invocation timing, more accurate parameter passing, and more reliable error handling.

4. The New Base Route: Generational Advantages from Parameter Scale

Opposite the post-training route is training a new, larger-scale base from scratch. A new model with trillion-level parameters typically has the following inherent advantages:

• Broader knowledge coverage;
• Stronger long-text modeling and cross-domain generalization;
• Deeper reasoning chain expansion.

Under the same post-training investment, the ceiling of a new base is often higher. For teams pursuing extreme performance with sufficient budget and no dependence on existing fine-tuning assets, the appeal of a new base is very direct.

But a new base also means higher costs:

• Pretraining requires substantial compute and time;
• Existing toolchains, prompt strategies, and fine-tuning samples need to be re-adapted;
• Actual benefits need to be re-evaluated against deployment costs.

Therefore, whether to adopt a new base is not just a question of “who scores higher” — it is an engineering decision about return on investment.

5. Comparing the Applicable Scenarios of the Two Routes

5.1 Scenarios Suited to Deep Post-Training

If a team has already accumulated substantial experience on an open-source base, including:

• Tuned prompt templates;
• Labeled domain fine-tuning data;
• An adapted inference serving pipeline;
• A stable automated evaluation set.

Then continued investment in the post-training stage can typically deliver perceptible capability gains at lower cost. Especially when the business demands high coding stability, tool calling, and task completion rates rather than topping every public leaderboard, the post-training route may be more pragmatic.

5.2 Scenarios Suited to the New Base Route

If the team faces situations such as:

• The existing base frequently hits a capability ceiling on multi-turn complex reasoning;
• Recall and comprehension in long-context tasks are clearly insufficient;
• The business requires stronger cross-lingual and cross-domain综合 capabilities;
• The adaptation cost brought by model migration is acceptable.

Then switching to a stronger new base may be more worthwhile than continuing to squeeze the old one.

6. How to Make the Judgment in Technical Selection

It is recommended to evaluate from the following dimensions:

6.1 Task Complexity

Break your business down into several typical tasks and evaluate the model’s performance on “single-turn generation,” “multi-step reasoning,” “long-horizon execution,” and “constraint adherence” separately. If the bottleneck is concentrated in the latter two, post-training often brings substantial improvement.

6.2 Dependence on Existing Assets

Count how many prompts, evaluation data, fine-tuning scripts, and toolchains currently depend on the existing model. The higher the asset dependence, the greater the hidden cost of switching bases.

6.3 Performance Targets and Budget

Clarify the quantitative expectations for performance improvement. If a capability improvement can bring noticeable growth in business metrics, choosing a new base is more reasonable; if the goal is simply for the model to make fewer errors and be a bit more stable on existing tasks, post-training may be sufficient.

6.4 Deployment Environment Constraints

Parameter scale directly affects inference cost and deployment difficulty. Even if a 3-trillion-parameter model is open-sourced, it does not mean most teams can run it at low cost. In actual selection, inference latency, throughput, memory footprint, and concurrency capacity must be included in a unified evaluation.

7. Future Trend: The Division of Labor Between Pretraining and Post-Training Will Become Clearer

As model capabilities move into deeper waters, competing purely on parameter scale is no longer the only path. The industry is forming a clearer stratification:

• The base is responsible for providing the “knowledge foundation”;
• Post-training is responsible for shaping “behavior patterns”;
• The application layer is responsible for building “task context.”

Under this trend, post-training scaling will become an important fundamental skill in large model engineering. Enterprises do not necessarily need to chase the latest base every time, but they do need the ability to continuously optimize the behavior of existing models.

For developers, rather than rushing to follow a particular model, it is better to first answer clearly:

• Where is the core bottleneck of my task?
• Which upgrade path can solve this bottleneck fastest?
• Can existing assets support a smooth migration?

Once these questions have clear answers, which route to choose is no longer a slogans debate but a rational engineering judgment.