Two Divergent Paths in Domestic LLM Development: Post-Training Saturation vs. Massive Base Models

The LLM race of the past two years has produced a curious phenomenon: on one side, base models with ever-expanding parameter counts setting new records; on the other, deep-optimization models that keep the base model unchanged and grind away exclusively at the post-training stage. These two seemingly opposed approaches actually represent two different answers to a fundamental question: where does the upper bound of intelligence come from? The former believes in the continued validity of scaling laws; the latter bets that reinforcement learning still has vast untapped headroom. This article examines two representative technical approaches as case studies, analyzing their differences, costs, and applicable boundaries to provide reference for developers making model selection and engineering decisions.

1. The Divide: Swapping the Base vs. Keeping It Fixed

To understand this matchup, we first need to clarify an implicit hierarchy in the industry: pre-training determines the lower bound of model capability, while post-training determines the ceiling a model can reach.

Path A: The massive base model approach. The logic here is straightforward—use larger parameter counts, more pre-training data, and greater compute investment to directly raise the model’s capability starting point. A base model at the trillions-of-parameters level means broader knowledge coverage, a stronger general reasoning foundation, and a larger “moldable space.” The costs are equally obvious: high training expenses, long iteration cycles, and the fact that a single base model rebuild often means months of investment with irreversible failure risk.

Path B: The post-training deep-dive approach. The judgment here is that—with base model potential not yet exhausted—scaling at the reinforcement learning stage can fully serve as a first-stage rocket. Core levers include: long-horizon task environments at ten times or greater scale, richer and more diverse environment types (terminals, code repositories, cross-tool collaboration, etc.), and extended post-training time. Simply put, this means shifting compute from “stacking pre-training” to “stacking post-training,” letting the model repeatedly immerse itself in real engineering task chains.

One noteworthy detail: models following Path B often explicitly state that “the base model is completely unchanged”—all the real effort goes into the post-training stage. This statement itself is a declaration of technical philosophy: pre-training is not the only source of capability.

2. Capability Focus: Why Both Paths Target Terminals and Coding

Regardless of which path they take, the capability announcements for this recent batch of models are highly concentrated in two areas: coding ability and agent capability. This is no coincidence.

Coding and terminal scenarios are currently the most commercially valuable and most easily quantifiable deployment scenarios for large models. And these two scenario types happen to place extremely high demands on post-training—they are not single-turn Q&A but long-horizon, multi-step, cross-tool task chains. Whether a model can continuously complete dozens of operations in a command-line environment without breaking down depends on how many real task environments it has seen during post-training.

Consequently, the evaluation battleground for both paths converges on the same point: agent testing in terminal/CLI environments becomes the key benchmark. The common feature of such benchmarks is that they simulate real developers working in the command line, examining the model’s tool invocation, state tracking, error recovery, and long-horizon planning capabilities. Whoever leads on these benchmarks is closer to the “can actually do work” positioning.

From this perspective, the two paths converge on capability goals; the divergence lies only in the path to achievement: one relies on a larger base model naturally possessing a stronger general reasoning foundation, while the other relies on the density and duration of post-training environments to squeeze existing base model potential to its limit.

3. Engineering Cost Comparison: Expense, Cycle, and Uncertainty

For developers and enterprises, route selection is essentially a trade-off of engineering costs.

The cost structure of the massive base model path exhibits a “heavy upfront investment, amortized later” pattern. Base model training requires enormous one-time investment, but once formed, the marginal cost of subsequent fine-tuning and scenario adaptation is relatively manageable. The risk: base model training cycles are long, potentially missing market windows; and if base model capabilities mismatch the target scenario, the cost of starting over is extremely high.

The cost structure of the post-training deep-dive path is “continuous investment, rapid iteration.” Since the base model remains unchanged, each version update can focus on the post-training pipeline, significantly shortening iteration cycles and lowering trial-and-error costs. However, this path’s ceiling is constrained by the base model itself—if the base model has hard deficiencies in certain capability dimensions, post-training can only mitigate, not cure. Additionally, building long-horizon task environments is itself heavy engineering: it requires large numbers of real task chains, diverse environment simulations, and stable reinforcement learning training infrastructure.

One easily overlooked point: post-training scaling depends far more on data engineering and environments than pre-training scaling. Pre-training can rely on stacking corpora; post-training must rely on “building environments”—the realism, diversity, and difficulty gradient of environments directly determine the effectiveness of reinforcement learning. This is why teams following this path often emphasize “long-horizon task environments at ten times or greater scale” and “richer and more diverse environment types.”

4. Open-Source Strategy: A Variable Beyond Capability

In this matchup, open-source posture is a variable that cannot be ignored. Against the backdrop of some vendors tightening weight availability, maintaining open source is itself a product strategy choice.

For developers, open source means private deployment, fine-tuning, and auditability—especially in coding and agent scenarios that require deep integration into existing engineering pipelines, where the value of open weights far exceeds pure API calls. For enterprises, open-source models reduce vendor lock-in risk and facilitate localized modifications under compliance requirements.

Therefore, when evaluating a technical path, one cannot look only at benchmark scores but must also consider whether its delivery format matches real usage scenarios. A model that leads on benchmarks but is closed-source and difficult to integrate, versus one that scores slightly lower but is open-source and deeply customizable, may have completely opposite cost-effectiveness for different teams.

5. Selection Guidance: By Scenario, Not by Score

Based on the above analysis, here are several selection considerations for developers:

First, clarify task chain length. If the core scenario is short-horizon, single-turn Q&A or simple code completion, the gap between the two paths may not be significant—prioritize integration cost and inference cost. For long-horizon, multi-step, cross-tool agent tasks, focus on the model’s performance on terminal/CLI benchmarks and the realism of its post-training environments.

Second, assess whether base model deficiencies are acceptable. Models following the post-training deep-dive path have capability upper bounds constrained by their base models. During selection, conduct targeted evaluations on key capability dimensions relevant to your business to confirm the base model has no hard deficiencies that cannot be remedied through fine-tuning.

Third, incorporate open-source and deployment format into the cost model. Private deployment, fine-tuning requirements, and data compliance requirements will all significantly change the actual total cost of ownership for both paths. Benchmark scores are only a starting point, not the destination.

Fourth, focus on iteration rhythm rather than single-point versions. The advantage of the post-training path lies in fast iteration—during selection, examine the team’s version update frequency and the maturity of their post-training pipeline. The advantage of the massive base model path lies in a thick capability foundation—during selection, examine the base model’s rebuild cycle and long-term roadmap.

6. Conclusion

“Keeping the base fixed” and “massive base models” are not mutually replacing relationships but two resource allocation strategies under the same goal. The former bets on the scaling potential of the reinforcement learning stage; the latter bets on the continued validity of scaling laws. For developers, what is truly valuable is not taking sides but understanding each path’s capability boundaries and engineering costs to make matching choices in specific scenarios.

The capability competition in large models is shifting from a “parameter race” to a “training methodology race.” When the base model is no longer the only variable, post-training engineering capability—environment construction, task design, reinforcement learning stability—will become the new watershed distinguishing team quality. The real spectacle of this matchup may not be about whose scores are higher, but about which path can more quickly convert benchmark advantages into real productivity.