The Limits of Post-Training Scaling: Can You Approach the Capability Ceiling of Larger Models Without Switching Base Models?

1. Two Paths for Improving Large Model Capabilities

The evolution of large model capabilities broadly follows two paths. One is to directly expand parameter scale and data scale during pre-training, replacing or rebuilding the base model. The other is to further unlock the existing potential of a base model through post-training methods such as instruction fine-tuning, preference alignment, reinforcement learning, and tool-use training.

The former is typically regarded by the industry as a “fundamental upgrade,” because increasing parameter scale and data scale directly changes a model’s memory capacity, knowledge coverage, and generalization boundary. The latter is often underestimated, sometimes dismissed as “parameter tuning” or “engineering patching.”

In real-world model iteration, however, upgrading the base model is not always the only option. When the pre-training stage has already accumulated a large amount of high-quality knowledge representation, but the model has not fully released its capabilities in task execution, tool use, long-horizon planning, or code generation, the marginal returns from post-training may far exceed those from rebuilding the base model. This is especially true when training budget, data assets, and engineering stability are already heavily concentrated on a particular base model version. How to “squeeze out” the existing model’s potential then becomes a very practical technical question.

2. Why Post-Training Scaling Deserves Serious Attention

Pre-training determines the lower bound of a model’s knowledge, while post-training affects how high the model’s capability ceiling can reach.

An intuitive analogy: the pre-training stage is like having the model complete extensive general reading and knowledge memorization, while post-training is like placing the model in a specialized work environment where it experiences real tasks, error feedback, strategy adjustments, and tool collaboration. The former lets the model “know,” while the latter lets the model “apply.”

The key to post-training scaling lies in three dimensions:

1. Quantity and diversity of task environments: exposing the model to terminal operations, code repository modifications, cross-tool collaboration, long-document understanding, multi-turn interaction, and other environments;
2. Length and complexity of task chains: expanding from single-turn Q&A to long-horizon tasks that require planning, execution, verification, and correction;
3. Training compute and time investment: continuously improving policy stability through more rounds of reinforcement learning and preference optimization while keeping the base model unchanged.

When these three dimensions are scaled simultaneously, the model may show leaps in capabilities such as programming, agent operation, and safety policy—and these leaps do not depend on an increase in parameter count.

3. Typical Problems of Not Switching Base Models and How Post-Training Addresses Them

Many teams face a practical problem when deciding whether to switch base models: a new base model may have more parameters and a higher theoretical ceiling, but it also brings higher inference costs, longer adaptation cycles, more complex deployment migration, and short-term capability regression risks.

In such cases, post-training can serve as a lower-risk upgrade path. It often delivers clear benefits in the following scenarios:

• The base model already has strong language understanding and code generation abilities, but tends to be “all talk and no action” in real engineering environments;
• The model performs well on single-turn tasks but frequently loses its objective in multi-step tasks;
• The agent lacks a stable action policy when calling tools, leading to repeated calls, incorrect calls, or ignoring environment feedback.

To address these problems, post-training is not merely about increasing data volume; it requires reorganizing training objectives. For long-horizon tasks, for example, reward signals covering task completion rate, step validity, and error recovery ability can be introduced during training. For tool use, sandbox environments can be built so the model learns stable policies through controlled feedback.

4. Post-Training Practices in Coding Agents

Coding agents are an important scenario for observing the effects of post-training scaling. The model needs to simultaneously possess code understanding, file navigation, command execution, error localization, and fix verification abilities—capabilities that are difficult to fully acquire from static code corpora.

In practice, the following approaches can enhance post-training results for coding agents:

1. Real engineering task-chain training: instead of using isolated code completion samples, build complete task trajectories that include “read code—locate problem—modify files—run tests—retry on failure—final pass”;
2. Environment feedback modeling: incorporate terminal output, test errors, static check results, and other feedback as part of policy learning;
3. Cross-tool collaboration samples: train the model to switch context among text editors, version management, build scripts, and dependency management tools;
4. Long-task memory and state tracking: guide the model to continuously maintain task state during long-horizon modifications to avoid objective drift.

Compared with simply using a larger parameter base model, this combination of “environment + feedback + policy optimization” can improve program execution success rates in a shorter cycle and reduce dependence on a specific base model.

5. Safety Capabilities and the Boundaries of Post-Training

Beyond coding ability, safety capability is also an important post-training objective. When facing network tasks, the model needs to accurately identify risk boundaries, avoid executing dangerous commands, and maintain effective operations in authorized testing scenarios.

Post-training can introduce safety constraint rewards, allowing the model to form a stable policy between “completing the objective” and “avoiding overstepping boundaries.” This type of training does not depend on parameter scale; it relies more on scenario definition, failure sample filtering, and careful design of policy gradients.

It is worth noting that safety capability training cannot rely solely on “blocklists” or fixed rules, otherwise the model may become overly conservative or bypass restrictions through pattern matching. A more effective approach is to use environment feedback so the model understands the consequences of actions and develops generalizable risk management capabilities.

6. The Value of Open-Source Strategy in the Post-Training Era

Competition between open-source and closed-source models has long centered on parameter scale. But as post-training gradually becomes a mainstream path for capability improvement, the logic of open-source competition is also changing.

For the open-source ecosystem, a transparent, reproducible, and fine-tunable training process means the community can continuously accumulate post-training assets on the same base model. A stable and long-maintained base model may actually have greater engineering value than frequently switching to new base models, because downstream teams do not need to repeatedly adapt deployment pipelines, inference frameworks, and toolchains.

With the base model unchanged, teams can invest more effort in environment construction, reward modeling, trajectory filtering, and evaluation systems. The technical accumulation formed in this way is not only effective for a single model but can also be reused in subsequent versions, creating a longer-term compounding effect.

7. Practical Limits of Post-Training Scaling

Although post-training can unlock considerable potential on a given base model, it is not a panacea.

First, the base model’s knowledge capacity still has an upper limit. If the model did not receive sufficient coverage of a certain type of knowledge during pre-training, post-training can hardly fill the knowledge gap from nothing. Second, post-training depends heavily on high-quality environments and feedback signals. Poor environment design and coarse reward modeling can easily cause the model’s policy to overfit to the training environment and fail to generalize to real scenarios. In addition, the returns from post-training gradually diminish as training investment increases. Once the model approaches its policy bottleneck, the cost-effectiveness of continuing to stack compute may decline significantly.

Therefore, post-training scaling is better suited as the main path during the stage when the base model’s potential has not yet been exhausted, rather than as a long-term complete replacement for base model upgrades.

8. Conclusion: Post-Training Is Today’s “High-Certainty Upgrade Path”

Without switching base models, approaching the capability ceiling of larger-parameter models through post-training scaling is a high-certainty upgrade strategy under current technical conditions and cost structures. It reduces engineering migration risk, shortens iteration cycles, and can achieve considerable improvements in key scenarios such as coding agents, long-horizon tasks, tool use, and safety policy.

This does not mean base model upgrades are no longer important. A more reasonable judgment is: pre-training scale determines the capability lower bound, while post-training determines the degree to which capabilities are realized. The two paths will ultimately move toward synergy rather than replacing each other.

For model R&D teams and AI application teams, the more pragmatic choice may be: when the base model is sufficiently stable and its capability foundation is solid, prioritize fully realizing the value of existing assets through post-training; only initiate a base model replacement when there is a structural deficiency in knowledge coverage or reasoning depth. This allows teams to maintain a good competitive rhythm while avoiding the high costs and high volatility of “upgrading for the sake of upgrading.”