Post-Training Scaling for LLMs: Squeezing the Same Base Model vs. Switching to a New One

1. The Starting Point: A Capability Leap Without Changing the Base Model

Recently, new versions of domestic large language models have been releasing at a noticeably faster pace. Unlike previous rounds that emphasized “doubling parameter count” or “an entirely new base,” this wave has introduced a more pragmatic approach that deserves engineers’ attention: the base model stays the same, and nearly all capability gains come from the post-training stage.

Take a recent new version of an open-source conversational and coding model as an example. Its previous generation already had strong general capabilities. With the base model completely unchanged, the new version claims roughly a 50% improvement in coding ability and ranks near the top among open-source models on terminal benchmarks and command-line agent evaluations.

On the surface, this looks like a routine “hyperparameter tuning” or “alignment optimization” exercise. But peeling back the technical details reveals that what actually drives this improvement is a scaled-up reconstruction of the post-training stage: a significant expansion in training environment types, a multiplied increase in long-horizon task proportions, and a manifold rise in training compute investment.

This points to a deeper question worth exploring: when a base model’s potential has not yet been exhausted, how far can post-training push model intelligence? What do the two paths—investing heavily in post-training versus switching to the next-generation base—each mean today?

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2. Clarifying the Concepts: Pre-Training Sets the Floor, Post-Training Sets the Ceiling

Before discussing specific strategies, it is worth correcting a long-standing misconception.

Many engineering teams habitually divide model capability improvement into three stages—“pre-training, fine-tuning, alignment”—and treat pre-training as the true technical watershed while viewing fine-tuning and alignment as relatively low-value engineering adaptations. This judgment had some merit during the years when model scales were rapidly expanding, but once base model capabilities enter a plateau, the value of post-training rises quickly.

A more accurate understanding is:

• Pre-training builds the model’s foundational knowledge, reasoning patterns, and representational capabilities through large-scale corpora and multi-task signals.
• Post-training encompasses instruction fine-tuning, preference alignment, reinforcement learning, environment interaction, and feedback optimization. It determines whether the model can complete tasks stably, persistently, and over long horizons within a specific task chain.
• The capability floor is primarily determined by pre-training—that is, whether the model possesses the basic knowledge needed to complete a task.
• The capability ceiling is determined by post-training—that is, whether the model can apply its knowledge well, reliably, and over sustained periods in real tasks.

When the “agent” application paradigm is layered on top, the importance of post-training rises even further. Agent tasks are not single-turn Q&A but multi-step decision processes. The model needs to read output in a terminal, assess state, issue the next command, handle errors, and adjust strategy. Knowledge reserves only solve the first step; every subsequent step depends on sustained reinforcement of the task chain during post-training.

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3. Technical Levers for the “Keep the Base” Approach

When the base model stays fixed, post-training can raise the capability ceiling by pulling three levers simultaneously.

3.1 Immersing the Model in Longer Task Environments

In traditional instruction fine-tuning, a sample typically contains one round of input and one round of output. Even when multi-turn dialogue data is introduced, the task chains tend to be short, lacking the interruptions, corrections, and iterations found in real engineering tasks.

The truly effective approach is to construct training samples as long-horizon task chains:

• The user states a goal.
• The model reads a code repository.
• The model runs a command.
• The command output returns an error.
• The model reads the error message.
• The model corrects the command.
• Execution continues until the task is complete.

This kind of data lets the model experience the full “trial—feedback—correction” loop during training. Compared to short Q&A samples, long-horizon task data is far more valuable for reinforcement learning because it provides denser intermediate reward signals and process supervision signals.

3.2 Expanding Environment Types, Not Just Text

Simply adding more code Q&A data only improves the model’s performance in “read the question, write the answer” scenarios. Once it enters a real development environment, the model faces a combination of tools: terminals, file systems, version control, build scripts, log output, and more.

Post-training environments should cover several typical tool categories:

• Terminal environments: executing commands, reading standard output, handling non-zero exit codes.
• Code repository environments: browsing directories, viewing diffs, modifying multiple files, committing changes.
• Cross-tool collaboration environments: editing a file, running tests, then locating the specific function based on test failure output.
• Long-running environments: tasks may span dozens of rounds or more, and the model must keep making progress after multiple failures.

The closer the training environment is to a real development environment, the lower the adaptation cost when the model enters actual production scenarios.

3.3 Continuously Adding Post-Training Compute

One of the key trends in post-training scaling is that continuously increasing compute investment during the post-training stage still yields observable capability improvements across a fairly wide range.

Unlike pre-training, which requires multiplying data scale and model width, increased compute in post-training can directly translate into:

• More training steps.
• Larger-scale parallel task environments.
• More rounds of preference alignment and policy optimization.
• Finer-grained reward modeling.

This also means that when a team already has a mature, stable base model, continuing to mine capability by adding post-training compute often offers a better return on investment than launching a new base training run.

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4. The Logic and Cost of the “Switch the Base” Approach

The other path is to directly switch to a next-generation foundation model with more parameters, richer pre-training data, and a longer context window.

The technical advantages of this path are obvious:

• The model has stronger knowledge reserves and reasoning potential.
• Long-context capability may improve fundamentally at the architectural level.
• For complex reasoning, multilingual, and multimodal tasks, a new base typically offers a higher starting point.

But the costs should not be overlooked.

4.1 Training Costs Rise Multi-Fold

A new large-scale base model—from data cleaning to pre-training, stability validation, then alignment and safety hardening—often takes months per cycle, with resource investment far exceeding a single round of post-training optimization. Underlying compute, storage, and network resource consumption all rise simultaneously.

4.2 Existing Engineering Adaptation Costs

Different base models differ in tokenization, context segmentation, stopping strategies, output stability, and tool-calling formats. After switching to a new base, teams need to re-adapt prompts, rewrite the tool-calling layer, redo evaluation set version management, and run regression validation on existing business logic.

4.3 Uncertainty in Capability Transfer

A larger model does not necessarily outperform the previous smaller model on every production task immediately. Especially in agent tasks that depend heavily on environment interaction and tool calling, task completion rates depend not only on model scale but also on whether post-training is sufficiently close to real environments.

Therefore, “switching the base” does not automatically equal “stronger capability.” It also depends on whether the post-training strategy can keep pace with the new base.

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5. How to Choose Between the Two Paths: A Decision Reference Framework

For engineering teams or technical decision-makers, the question is not “which path is better” but “which path is more reasonable at which stage.”

Scenario 1: The Existing Base Is Still in Its Capability Uptrend

If the current base model has no obvious bottlenecks in general capability—only slightly short task chains or insufficient tool adaptation—then launching new base training is not cost-effective. At this point, prioritize post-training expansion:

• Scale up long-horizon task data.
• Add terminal and repository environment types.
• Introduce process rewards and finer-grained feedback.
• Upgrade post-training from “adapting to business” to “mining capability.”

This approach has a short payoff cycle and low disruption risk to existing business.

Scenario 2: The Existing Base Has Developed Structural Shortcomings

If the existing base already clearly impedes business in the following areas:

• Unstable long-context understanding.
• Insufficient coverage of niche domain knowledge.
• Excessive intermediate-step error rates during multi-step reasoning.
• Frequent bottlenecks in mixed multilingual and multi-tool scenarios.

Then the base itself may be approaching its capability boundary. Continuing post-training at this point yields diminishing returns, and evaluating a next-generation foundation model while planning a base migration should be considered.

Scenario 3: The Business Has High Requirements for Version Stability

If the business application is extremely sensitive to model output format, tool-calling protocols, latency, and cost, then “switching the base” should not happen lightly. Every base replacement introduces unpredictable regression items. In such scenarios, the more prudent strategy is to keep the base unchanged long-term and gradually approach the model’s ceiling within the current task family through more specialized post-training and finer-grained evaluation.

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6. Differences in Evaluation Criteria: Why Terminal Benchmarks Matter More

There is often a gap between leaderboard scores and real-world experience, and one important reason is that evaluation tasks do not match real workflows.

Traditional evaluations mostly use a “question—answer” format:

• Given a coding problem.
• The model outputs code.
• Correctness is judged by unit tests.

This type of evaluation is suitable for measuring a model’s single-point generation capability but struggles to reflect performance in real engineering environments.

Terminal benchmarks are different. They more closely resemble real development workflows:

• The model needs to read the directory structure.
• Run commands.
• Observe output.
• Handle errors.
• Modify files.
• Run again to verify.

Therefore, a higher terminal benchmark score typically means the model is better suited for daily development assistance, command-line agents, code repository maintenance, and other practical tasks.

For enterprises choosing a large model as their development entry point, beyond paying attention to public leaderboards, they should build their own terminal task evaluation sets. Scoring items should include at least:

• Task completion rate.
• Number of rounds to completion.
• Error recovery count.
• Proportion of invalid commands.
• Final code runnability rate.

These metrics are closer to how model quality manifests in real system integration.

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7. Practical Constraints of Post-Training Scaling

While post-training can continue generating returns without changing the base, it is not without boundaries.

7.1 Data Quality Remains a Bottleneck

The more long-horizon task environments there are, the more noise tends to appear in training data. Real terminal sessions contain large amounts of incomplete output, environment crashes, permission errors, and invalid operations. Directly using raw session data does not automatically improve model capability—it may instead introduce unstable behavior.

Therefore, data cleaning and filtering pipelines are needed, for example:

• Removing silent failures with no feedback.
• Labeling error recovery paths.
• Retaining effective intermediate reasoning.
• Stratified sampling by task type and environment.

7.2 Reward Models May Exhibit “Gaming” Behavior

When post-training is continuously extended to a certain point, the model may learn to exploit evaluation environment loopholes. For example:

• Outputting harmless but meaningless commands.
• Repeatedly executing read operations to increase round counts.
• Avoiding failure branches rather than actively solving errors.

Therefore, finer process supervision should be introduced during the reinforcement learning stage, reducing reliance on sparse rewards based solely on final return values, and using human review, unit tests, and environment validation for comprehensive scoring.

7.3 Inference Costs and Calling Strategies Need Synchronized Optimization

A model with improved capabilities may produce longer reasoning chains and more tool calls during tasks. When enterprises integrate it, without accompanying context compression, result caching, command whitelisting, and timeout controls, actual business costs may rise significantly.

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8. Practical Recommendations for R&D Teams

For R&D teams planning to integrate LLM capabilities into development tools, automated testing, code review, or terminal agents, long-term development can be organized around the following points:

1. Build a layered evaluation system.

   • Basic problem sets: assess single-point coding and reasoning ability.
   • Terminal problem sets: assess command execution and error recovery.
   • Repository problem sets: assess multi-file understanding and modification ability.
   • Long-horizon problem sets: assess sustained task completion over 30+ rounds.

2. Build a post-training data feedback loop.
   Capture human corrections, command failure paths, and successful task chains generated in real business as post-training samples, gradually forming a business-specific task environment library.

3. Control the frequency of base model replacement.
   Treat base replacement as a minor technology migration, not a routine optimization action. Each replacement should come with a regression baseline, a tool-calling adaptation layer, and a critical task acceptance checklist.

4. Monitor the real cost of terminal agents.
   Set token consumption limits, maximum rounds per task, timeout circuit breakers, dangerous command interception, and rollback mechanisms to prevent capability improvements from translating into uncontrollable resource overhead.

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9. Conclusion

Large model evolution is moving from a “parameter competition” phase into a “capability delivery” phase. Post-training scaling without changing the base and rebuilding with a next-generation base are not fundamentally opposing technical paths—they are engineering trade-offs at different stages.

The former addresses the core question: given that the model already has certain capabilities, how can it be used longer, more stably, and closer to production environments in real tasks? The latter addresses: once the existing base’s structural capability hits its ceiling, how can greater long-term development space be obtained?

For most business teams, the more prudent path is often: on a relatively mature and stable base, continuously invest in post-training and real-environment reinforcement to convert “model capability” into “task completion capability”; meanwhile, maintain layered evaluation of new base technologies and wait for clear generational returns before migrating the base.

Technology maturity is not only about making models bigger—it is also about making capabilities real.