Comparing Technical Pathways and Engineering Practices for LLM Post-Training Scaling

Recently, the competition in the foundation model space has noticeably shifted from “stacking parameters and changing base models” to “competing on post-training and unlocking potential.” While some teams continue to push towards new foundation models with trillions of parameters, others opt to stick with their existing base models, relying on intensive post-training to elevate performance in coding, agentic, and long-horizon tasks. Neither approach is inherently superior, but the technical judgments and engineering paths behind them warrant closer examination.

Rather than reviewing specific products, this article focuses on the general technical theme of “post-training scaling.” It analyzes the design logic, training methods, capability boundaries, and deployment strategies to help developers and algorithm teams clearly determine: when the foundation model remains unchanged, along what dimensions can we continue to enhance model value.

I. The Technical Orientation Behind the Two Pathways

Current LLM R&D can generally be divided into two approaches:

1. Foundation-First Approach
   This route directly improves the model’s foundational representation capabilities by scaling parameter size, increasing training data, and refining pre-training objectives. It typically involves longer training cycles, higher compute consumption, and stricter data quality requirements. The advantage is a higher capability ceiling, making it suitable for foundational general-purpose upgrades.

2. Post-Training-First Approach
   Built upon an existing foundation model, this route utilizes supervised fine-tuning (SFT), reinforcement learning (RL), long-horizon task training, and multi-environment interactions to make the model perform more stably on specific tasks. Its advantages include fast iteration cycles and relatively controllable costs, making it particularly suitable for building vertical capabilities in coding, agents, and safety.

The latter is not merely “hyperparameter tuning.” When the capabilities of the foundation model have not yet been fully unleashed, post-training scaling can yield highly significant leaps in performance. Pre-training determines what a model can learn, while post-training determines how well the model can utilize what it has already learned. The two are complementary, not substitutes.

II. What Else Can Post-Training Do Without Changing the Base Model?

If a team decides not to replace the foundation model, they can still dig deeper across the following three dimensions:

1. Training in Long-Horizon Task Environments

Traditional fine-tuning often relies on single-turn or short-context samples, but real-world engineering tasks usually consist of multiple continuous steps. For example, a complete code modification task might involve:

• Reading the repository structure
• Locating relevant files
• Understanding the existing implementation
• Modifying core logic
• Adding test cases
• Running and fixing errors
• Committing the change description

In such long-horizon task chains, the model needs not only to output correct code but also to possess state tracking, error recovery, and context memory capabilities. Introducing more long-horizon task environments during the post-training phase can effectively reduce the model’s tendency to “derail midway” in real-world usage.

2. Diverse Environments and Tool Interactions

Training in a single environment easily leads to path dependency. For instance, if a model is trained only in a simple terminal environment, it may struggle to adapt to complex scenarios like navigating code repositories, multi-file editing, or cross-tool collaboration.

A more robust approach is to diversify the training environments:

• Terminal command execution environments
• Code repository browsing and modification environments
• Multi-language mixed-project environments
• Constrained task environments, such as permission or network restrictions
• Tasks requiring external tool or API calls

The more diverse the environments, the stronger the model’s generalization across task types, leading to greater stability when deployed in actual business scenarios.

3. Compute Investment in the Reinforcement Learning Phase

Post-training scaling is not just about data; it also involves sustained compute investment. During the reinforcement learning phase, the model requires massive sampling, discrimination, and reward signaling—steps where computational costs are significantly higher than standard inference.

If a team can build a stable reward model and continuously accumulate high-quality feedback during training, the RL phase can become a critical lever for boosting model performance. Particularly in coding and agentic tasks, reward signals can be designed around verifiable, objective metrics, such as:

• Whether the code passes tests
• Whether execution results meet expectations
• Whether commands are successfully completed
• Whether the task concludes within a limited number of steps

These verifiable signals are easier to automate and scale than manual human preferences.

III. Engineering Practices for Post-Training Scaling

To execute post-training effectively without changing the foundation model, teams must advance synchronously across three areas: data, training architecture, and evaluation.

1. Build Automatically Evaluable Task Sets

Post-training efficacy heavily depends on the quality and coverage of training tasks. It is recommended to prioritize tasks with verifiable objectives rather than relying solely on manually annotated preference data.

A typical coding task sample can be structured as:

• Input: Repository structure + requirement description + test cases
• Output: Code modifications
• Automatic Reward: A weighted combination of test pass rate, code style, and execution time

This automated construction method not only reduces annotation costs but also enables faster iteration of the training process.

2. Introduce Curriculum Learning Strategies

Not all tasks are suitable for introduction at the very beginning of training. For high-difficulty, long-horizon tasks, curriculum learning can be applied, gradually transitioning from short to long tasks:

• Phase 1: Single-file modifications, simple command execution
• Phase 2: Multi-file modifications, understanding repository structure
• Phase 3: Cross-module collaboration, error recovery, multi-turn interactions
• Phase 4: Open-ended tasks, autonomous step planning

This progressive arrangement allows the model to stabilize its foundational capabilities before gradually adapting to more complex task chains.

3. Iteration and Calibration of Reward Models

During the reinforcement learning phase, the quality of the reward model directly dictates the final model’s performance. For coding tasks, it is advisable to prioritize rule-based rewards, supplemented by model-based rewards:

• Rule-based rewards: Tests passing, execution errors, step limits exceeded
• Model-based rewards: Judging code readability, solution rationality, etc.

This approach preserves objectively verifiable constraints while supplementing subjective quality aspects that cannot be fully automated.

4. Separate Training and Evaluation

Many teams fall into the trap where “the training set increasingly resembles the test set.” To ensure evaluations are authentic and effective, training tasks and final evaluation tasks must be isolated:

• Use different repositories for training and testing samples
• Hold out unseen new dependencies or tools for evaluation
• Introduce a small amount of manual review during evaluation to prevent the model from overfitting to rules

Only by separating training and evaluation can the capability improvements brought by post-training be truly transferable.

IV. Realistic Assessment of Capability Boundaries

Although post-training scaling can significantly enhance model performance in coding, agents, and safety, its capability boundaries must be acknowledged:

• Not changing the base model does not mean infinite improvement. When the foundation model itself has clear shortcomings in complex reasoning or knowledge coverage, post-training alone cannot fully bridge the gap.
• Long-horizon task performance requires continuous validation. A model may perform well within its training distribution but still degrade when faced with entirely new task types.
• Real business environments are far more complex than training environments. Online task routing, permissions, data standards, and exception scenarios often exceed the coverage scope of training data.

Therefore, a more pragmatic strategy is to treat post-training scaling as a phased capability accelerator rather than a complete substitute for foundation model upgrades. Using both pathways in parallel yields a more reasonable balance between cost, speed, and final outcomes.

V. Recommendations for Implementation

For teams planning the engineering deployment of LLMs, the following points are worth considering:

1. Clarify capability goals before deciding on a training strategy. If general foundational capabilities are lacking, prioritize a base model upgrade; if performance is unstable on specific tasks, prioritize post-training investment.
2. Establish a library of verifiable task examples. Whether for coding, agents, or safety, a library of verifiable examples is a foundational asset for post-training.
3. Focus on building a training and evaluation loop. Feed evaluation results back into data filtering, reward design, and environment setup to form a continuous iteration cycle.
4. Reasonably control post-training costs. Compute investment during the RL phase needs to match expected returns; avoid blindly stacking compute without quality improvements.
5. Retain a proportion of manual review. Automated metrics cannot cover all issues, especially subjective dimensions like safety and usability.

Looking at overall industry trends, “keeping the base model and deepening post-training” and “upgrading the base model to push the limits” will likely continue to coexist. For developers, understanding the applicable scenarios of different strategies is far more valuable than chasing any specific model version. The core of model capability building always lies in how to achieve the results that best meet business needs at the minimum cost.