Pushing Post-Training Scaling to the Limit: Can a Fixed Base Model Match Next-Generation Super-Large Models?

Two Diverging Technical Routes

Over the past several months, a notable pattern has emerged in the large model community. On one side, teams are replacing the base model entirely and re-pretraining new generations with trillions of parameters. On the other, teams are keeping the base model completely unchanged and pouring all resources into the post-training stage, attempting to squeeze every last bit of capability out of the model they already have.

Behind these two routes lie two very different R&D philosophies.

Replacing the base means reinvesting in large-scale pretraining, with potential changes to data mixtures, vocabulary design, attention architecture, and distributed training strategies. Its advantage is a higher theoretical ceiling—new data and new architecture can deliver broader knowledge coverage and stronger long-range dependency modeling. But the costs are equally clear: long training cycles, enormous compute consumption, high stability risk, and the fact that alignment and reinforcement learning still have to be redone after pretraining completes.

Keeping the base and deepening post-training starts from a different premise: a base model’s capabilities are far from fully activated. Rather than betting resources on pretraining, this route invests in deeper, finer-grained scaling across reinforcement learning, preference alignment, environment interaction, and tool use. It offers shorter investment cycles, faster iteration, and more flexibility in responding to evaluation feedback and user needs.

Understanding this split requires a basic judgment: a model’s capability ceiling is determined by pretraining, but the ceiling you can actually reach is determined by post-training. In other words, pretraining sets the ceiling; post-training decides how close you can get to it. Once a base model has accumulated sufficient world knowledge and language understanding during pretraining, post-training shifts from a nice-to-have into the key lever for unlocking that ceiling.

What Post-Training Scaling Actually Scales

Post-training scaling is not simply “more epochs” or “bigger batches.” Based on publicly available technical material, three observable dimensions of expansion can be identified.

1. The Number and Complexity of Task Environments

Traditional post-training typically relies on fixed datasets, with supervised fine-tuning or preference optimization performed on pre-labeled samples. The most visible change in post-training scaling is the introduction of large numbers of real, interactive task environments.

Take terminal command execution as an example. A real environment observation may include the current working directory, the file listing, the exit code of the previous command, and stdout and stderr. The model must adjust its next action based on this dynamic information, and the environment state advances turn by turn as a result of the model’s own decisions. This “reason–act–observe” loop is fundamentally different from static, question-answering-style supervision.

As environments expand from simple commands to codebase navigation, cross-tool collaboration, and multi-step engineering task chains, the semantic depth of each sample increases substantially.

2. Reinforcement Learning Training Duration

With the base model fixed, extending RL training duration several-fold—or even by tens of times—can produce more stable policy convergence. This is not intuitive for large decoder models; many worry that long training runs lead to policy degradation or over-optimization. In practice, however, as long as environment diversity and reward model quality keep pace, longer training typically means more refined tool-calling trajectories and lower command error rates.

3. Reward Signal Density

Post-training scaling also shows up in the granularity of reward signals. The traditional approach assigns a single sparse reward only when the task is fully complete; improved approaches introduce process rewards—whether a command’s syntax is valid, whether intermediate outputs match expectations, whether a more concise solution was available. Denser reward signals substantially alleviate the gradient sparsity problem in long-horizon tasks, giving the model useful learning signal at intermediate steps.

One caveat: post-training scaling is not a universal fix. Its returns depend heavily on the quality of the base model’s pretraining. If the base already has clear weaknesses in fundamental coding ability, instruction understanding, or long-horizon memory, no amount of post-training will fully close those gaps. Post-training amplifies capabilities that already exist; it does not create them from zero.

What Benchmarks Reveal About Capability Distribution

Evaluating post-training scaling means looking beyond a single question-answering accuracy score toward evaluation dimensions that better reflect real development work. The most informative public benchmarks today focus on agent capabilities in terminal or command-line environments.

Terminal-Based Benchmarks

These benchmarks simulate a terminal environment and test whether the model can autonomously plan, execute commands, and fix errors in a target directory. Common metrics include task completion rate, average number of command steps, and recovery rate after the first failure.

Gains from post-training scaling are especially visible here. The terminal command space is relatively closed and its feedback signals are unambiguous, which makes it a natural fit for RL environment design. A typical task chain looks like this:

# Goal: fix a failing test in the repository
# Initial state: code repo + existing test files + error logs
# Model decision process:
1. read_file("pytest.log")          # Read the log to locate the failure
2. grep("Error", "pytest.log")      # Extract error patterns
3. read_file("src/utils.py")        # Inspect the relevant source file
4. edit_file("src/utils.py", ...)   # Apply the fix
5. run_command("pytest -q")         # Verify the fix

At every step, the model must judge whether the current information is sufficient, which file to read next, and whether the command arguments are appropriate. All of these decisions can be densely supervised through multi-turn reward signals in RL.

Agent Exam-Style Benchmarks

This type of benchmark packages multiple heterogeneous tasks into a single agent evaluation suite covering terminals, file systems, database operations, and text processing. The model receives a natural-language goal and must produce verifiable results within a constrained computing environment.

The advantage of post-training scaling here is that after seeing large numbers of complete “failure–correction–success” trajectories, the model develops stronger error-recovery strategies. The marginal benefit is more pronounced than simply adding parameters.

The Developer Experience in Coding

Another common evaluation lens is the “developer experience” in coding: the intuitive feel of using the model day to day. It does not map to a single metric but combines code correctness, command planning, error recovery efficiency, long-file handling, and cross-file understanding.

Some engineering teams report that, in coding scenarios and without changing the base, a model that went through large-scale post-training scaling delivered roughly a 50% perceptible improvement in command planning stability. That figure is not a standardized metric, but it points to a trend: post-training improvements in developers’ high-frequency workflows are often more noticeable than overall benchmark scores.

When Keeping the Base Model Makes Sense

To be clear, keeping the base model is not inherently more advanced, and replacing it is not meaningless. The real question is: under what conditions should post-training scaling be the preferred investment?

Condition 1: The Base Model Has Not Hit Its Ceiling

If pretraining data scale, model capacity, and training duration still leave significant headroom, post-training scaling usually offers a strong return on investment. Conversely, if the base is already near its ceiling on core capabilities, the marginal returns of further post-training decline.

Several signals can indicate whether a base has peaked: whether the answer distribution stays highly stable across many sampled generations; whether performance variance across prompting strategies shrinks; and whether the model shows clear generalization gaps on unseen, low-frequency tasks. If these signals all point to “stable but brittle,” post-training gains are approaching their limit.

Condition 2: The Application Scenarios Provide Clear Environmental Feedback

Post-training scaling depends on high-quality reward signals. Terminals, code repositories, compilers, and test suites are deterministic environments that naturally suit RL, because feedback at every step is verifiable. For open-ended writing or creative generation, reward signals are largely subjective, and the returns from scaling are far less certain.

Condition 3: The Model Requires Long-Term Maintenance and Fast Iteration

For model services that ship continuously and must stay close to developer needs, post-training scaling offers a faster iteration path. Compared with re-pretraining a new base, keeping the base stable means backend inference compatibility, evaluation pipelines, and safety alignment policies all carry over, dramatically reducing engineering costs.

Engineering Takeaways

The lesson of this route debate is not a static verdict on “which model is stronger,” but a set of more general decision variables.

First, evaluate agent pipelines, not single-point capabilities. Competition among large models is shifting from “getting answers right” to “completing complex tasks inside an environment.” Developers should not evaluate models solely on static datasets; they should build trial environments resembling terminal tasks and observe how robustly the model handles real state changes.

Second, treat error recovery as a first-class evaluation dimension. In long tasks, failing on the first attempt is the norm. A good agent must not only get things right in one shot but also quickly locate problems, roll back, and correct course after an error. One of the biggest benefits of post-training scaling is precisely that the model has seen enough failure trajectories to develop mature recovery strategies.

Third, post-training investment is not one-off. As real task environments and user feedback accumulate, continuous post-training scaling can serve as the core mechanism for long-term model operations. Experienced teams close the loop of “environment expansion → model training → benchmark evaluation → online canary rollout → data backflow,” allowing the model to keep gaining capability through each iteration.

Conclusion

The route of keeping the base model and pushing post-training to its limits has demonstrated practical capability in coding and agent scenarios that approaches—and sometimes rivals—much larger parameter models. The key is that it strengthens understanding of task environments, improves the quality of command planning, and hardens error recovery—exactly the capabilities developers perceive most directly in daily use.

That said, this route cannot amplify a base model’s ceiling without limit. As task complexity approaches the capability boundary established during pretraining, further performance gains slow considerably. The more pragmatic strategy, then, is not choosing one side but dynamically weighing four variables: the base model’s ceiling, the quality of environmental feedback, iteration cadence, and engineering cost.

For developers, rather than debating whether a given technical route is outdated, the key to smarter model selection and architecture decisions is understanding the conditions under which it works and the mechanisms by which it delivers gains.