Post-Training Scaling vs. Upgrading Base Models: Evaluating Technical Routes for AI Agents

Recently, the release pace of large language models (LLMs) has accelerated significantly, and the competitive focus has shifted from benchmark scores to real-world engineering capabilities. Especially in scenarios like coding assistants, terminal AI agents, and long-horizon task execution, a model’s ability to stably complete multi-step operations has become the metric developers care most about.

Against this backdrop, two distinct technical routes have emerged in the industry. One focuses on intensifying the post-training phase on an existing base model without changing its parameter scale or pre-training weights. The other involves directly switching to a larger base model to raise the performance ceiling through higher parameter capacity and stronger pre-trained representations.

These two routes are not a simple matter of right versus wrong. They represent two different judgments about the boundaries of model capabilities. One assumes the base model still has untapped potential, with the bottleneck lying in post-training. The other assumes the base model itself is approaching its representational limits and requires structural expansion.

This article systematically analyzes the differences, applicable scenarios, and potential gains of both routes across four dimensions: the mechanisms of post-training scaling, terminal AI agent capability evaluation, programming task performance, and engineering deployment costs.

I. Why the Post-Training Phase is Increasingly Critical

1. Pre-training Determines the Baseline; Post-training Determines the Ceiling

In LLM training, the pre-training phase primarily handles language modeling and knowledge compression, enabling the model to acquire general grammar, factual memory, and basic reasoning abilities. This phase is characterized by large scale and broad coverage, but its objective function is relatively coarse.

The post-training phase is different. It typically involves instruction fine-tuning, human feedback alignment, reinforcement learning (RL), and environmental interaction training. During this phase, the model learns how to better accomplish specific tasks, including following instructions, controlling output formats, invoking tools, and executing multi-step reasoning.

To use an analogy: pre-training is like completing basic education and building a knowledge reserve, while post-training is equivalent to practical on-the-job training. A newcomer with immense potential will struggle with complex tasks without sufficient project experience. Conversely, if foundational abilities are solid, high-intensity practical training can significantly elevate actual work performance.

2. Three Key Paths for Post-Training Scaling

Based on current industry practices, post-training scaling primarily unfolds along three directions:

1) Expanding Long-Horizon Task Training Environments

Traditional instruction fine-tuning mostly uses single-turn or short-horizon training samples, where the model completes one or two steps and receives feedback. However, in real-world engineering, a task often requires executing dozens or even hundreds of consecutive steps, such as locating an issue in a code repository, modifying multiple files, running tests, and continuing to fix errors based on test reports.

The core value of a long-horizon task training environment is teaching the model to maintain goal consistency within a long-term context, reducing mid-task confusion or goal drift. This type of training is particularly crucial for terminal agents and coding assistants.

2) Diversifying Environment Types and Interaction Modalities

If a model is trained only in a single type of environment, it easily suffers from insufficient generalization. For instance, a model trained solely on simple terminal commands might perform unstably when encountering real code repositories, multi-level directory structures, or cross-tool invocations.

Richer environment types—including terminal command execution, code repository browsing, cross-tool collaboration, and log analysis—can improve a model’s adaptability to different workflows. More importantly, different environments yield varied feedback signals, helping RL policies converge more effectively.

3) Extending Training Time and Compute Allocation

Compute allocation in the post-training phase is rapidly increasing. Previously, the industry widely viewed post-training as mere “few-shot tuning,” but now more teams are investing massive compute into reinforcement training. The reason is simple: the exploration and feedback loops in RL inherently require massive samples to converge stably.

When base model capabilities are sufficiently strong, investing more training time often continues to yield considerable performance gains. This is the underlying logic of why “post-training scaling” has become an independent technical route.

II. Terminal AI Agents as the Key Testing Ground

1. The Growing Importance of Agent Testing in Terminal Environments

Past LLM evaluations mostly focused on static Q&A and code generation. These tasks have clear inputs and outputs, requiring only “one-shot generation” from the model. Yet, real-world development involves much more than that.

Take terminal agents as an example. The model must accomplish given objectives within a command-line environment, such as:

• Finding files matching specific criteria in a directory;
• Locating and modifying scripts based on build errors;
• Installing dependencies and running test suites;
• Extracting exception information from logs and providing fix proposals.

These tasks require the model to possess the following capabilities:

• Understanding terminal state and feedback;
• Generating executable commands;
• Adjusting the next step based on output;
• Self-correcting upon failure.

These capabilities cannot be reflected through simple code completion tests; they must be evaluated in real or highly simulated environments.

2. Shifting Benchmarks from “Writing Code” to “Completing Tasks”

Previously, coding capability evaluations focused mainly on whether a model could generate correct, concise, and semantically appropriate code snippets. Now, the focus is shifting toward “task completion rates.”

For example, fixing a bug in a simple evaluation only requires the model to generate a modified code snippet. In contrast, a comprehensive evaluation requires the model to:

• Find the relevant file in the code repository;
• Analyze the problem and context;
• Generate a fix plan;
• Execute tests for validation;
• Make secondary modifications if necessary.

This workflow closely resembles real-world development and rigorously tests the model’s long-term planning and execution capabilities. Agent evaluations in terminal environments perfectly provide this “dynamic, multi-step, feedback-driven” testing ground.

3. Context Windows and Long-Horizon Tasks

Long-horizon tasks place higher demands on context windows. During multi-step operations, the model must continuously maintain an understanding of the initial objective, intermediate results, tool outputs, and historical actions.

Current mainstream LLMs generally support context windows at the 1M token level. This means a model can ingest tens of thousands of lines of code, multiple file contents, and massive terminal outputs at once. Window capacity is no longer a hard bottleneck; the more critical factor is whether the model can effectively utilize this information within a long context.

If post-training includes sufficient long-horizon task data, the model can gradually learn how to filter key content from massive historical information rather than being overwhelmed by noise.

III. Deep Mechanisms Behind Programming Capability Improvements

1. Advantages of Reinforcement Learning in Coding Tasks

Compared to simply imitating training data, RL allows models to learn through an “attempt-feedback-adjustment” loop. This is particularly meaningful for programming tasks because the correctness of code tasks can often be verified by objective standards, such as:

• Whether unit tests pass;
• Whether compilation succeeds;
• Whether runtime results meet expectations;
• Whether static checks detect type errors.

These automatable feedback signals provide high-quality training objectives for RL. By introducing a large volume of code tasks with automated verification during post-training, models can correct erroneous output patterns much faster.

2. Strengthening Planning via Long-Horizon Code Tasks

For simple single-file or single-function tasks, models can easily “get it right on the first try.” However, in real-world software projects, issues like cross-file modifications, interface adjustments, and state management require the model to plan before executing.

Including long-horizon tasks in post-training strengthens the model’s ability to reverse-derive steps from the task objective. For instance, during training, a model will experience the following workflow:

• Read the project structure;
• Locate target files;
• Understand existing implementations;
• Design a modification plan;
• Execute modifications;
• Run tests;
• Continue fixing based on feedback.

After experiencing this workflow multiple times, the model gradually internalizes a “read first, think next, execute, then verify” working mode. This change significantly improves the programming experience.

3. How Environmental Feedback Aids Debugging

Debugging capability is essentially an “observe-hypothesize-verify” loop. The model needs to infer the cause of an issue based on error messages, log outputs, and runtime results, and attempt a fix.

If the post-training environment provides sufficient debugging scenarios, the model can extensively practice locating problems based on feedback. Especially in terminal environments, error messages are usually quite explicit, providing clear supervisory signals for the model.

IV. The Benefits and Costs of Upgrading to a Larger Base Model

1. Potential Advantages of Larger Base Models

Larger-scale base models can accommodate more knowledge, cover more language patterns, and learn richer reasoning structures during pre-training. In certain complex reasoning tasks and few-shot generalization scenarios, larger base models typically exhibit stronger stability.

For example, in tasks requiring broad background knowledge, a large base model can directly invoke existing knowledge; in low-resource tasks, it also demonstrates better transfer capabilities. Therefore, upgrading to a larger base model often brings “out-of-the-box” capability improvements.

2. But a Bigger Base Doesn’t Automatically Solve Everything

However, an increase in base model parameters does not automatically translate into higher task completion rates. Without targeted post-training, even a massive base model can exhibit the following issues:

• Easily losing track of goals in long-horizon tasks;
• Unstable mastery of tool invocation formats;
• Inability to effectively use environmental feedback for self-correction;
• Outputs that may be too divergent and lack execution constraints.

These issues are rooted in the mismatch between task formats and training objectives, which cannot be solved simply by adding parameters.

3. Engineering Costs of Large-Scale Base Models

From an engineering deployment perspective, large-scale base models bring more significant resource consumption and performance overhead:

• Significantly increased memory (VRAM) requirements, especially in long-context scenarios;
• Increased inference latency, affecting interactive experience;
• Higher complexity in distributed deployment, leading to greater operational costs;
• In multi-tenant, highly concurrent cloud service environments, the unit throughput cost will also rise significantly.

For enterprise developers, if the model needs to be embedded into existing DevOps workflows or local development environments, these costs must be factored into the evaluation.

V. The Synergy Between Post-Training Scaling and Base Model Expansion

1. Not Mutually Exclusive Alternatives

Looking at technological evolution, post-training scaling and base model expansion are not always an either-or choice. In many cases, they advance alternately.

A reasonable approach is:

• Fully mine the potential of an existing base model through high-intensity post-training, first validating the effectiveness of task formats and training methods;
• When it is clear that the existing base model still shows obvious capability limits after multiple rounds of optimization, switch to a stronger base model;
• After the new base model goes live, continue applying the proven post-training pipeline to rapidly complete capability migration.

This strategy of “maximizing current potential before upgrading the base” can reduce the cost waste associated with blind switching.

2. How to Decide “Whether to Switch Base Models”

Technical teams can look for the following signals to determine if they should upgrade their base model:

• Despite continuously increasing post-training investment, improvements in multiple core metrics are plateauing;
• Although long-horizon task success rates are rising, error types are concentrated on basic comprehension or insufficient knowledge coverage;
• After diversifying environment types or extending training time, the model still cannot stably master certain basic tool usages;
• A “ceiling effect” repeatedly occurs on specific tasks, making it difficult to break through even with increased training scale.

If these situations occur, it indicates that the representational capacity or knowledge coverage of the current base model may be nearing its bottleneck, and expanding the base model scale can be considered.

Conversely, if model capabilities continue to improve by optimizing post-training data, adjusting RL strategies, and enriching training environments, then holding off on upgrading the base is the more economical choice.

3. Post-Training Experience is Reusable Across Base Models

Experience accumulated during post-training—including environment construction methods, feedback design, curriculum learning strategies, and sampling and replay methods—holds high reuse value. Even if a new base model is adopted in the future, these training assets can still play a role.

This is why post-training scaling itself is a capability requiring long-term investment. It is not a one-time parameter tuning, but a continuous process of building training infrastructure, data environments, and evaluation systems.

VI. Practical Recommendations for Developers and Enterprises

1. Prioritize “Task Completion” When Evaluating Models

Do not focus solely on the quality of one-shot code generation; pay more attention to the model’s performance across a complete task chain. Consider building an evaluation set that reflects actual work, including:

• Locating issues in a repository and modifying code;
• Executing a series of commands in a terminal and handling exceptions;
• Completing a small feature iteration across multiple files;
• Locating and fixing build issues based on error logs.

These evaluations more authentically reflect the model’s value in the development workflow.

2. Focus on Self-Correction Capabilities

In actual development, it is rare for the first output to be completely correct. Whether a model can adjust its strategy based on error feedback in a timely manner is a crucial factor affecting user experience. During selection, heavily test the model’s performance in the following areas:

• Automatically adjusting code based on compilation errors;
• Correcting parameter configurations based on runtime logs;
• Choosing alternative solutions when a command fails;
• Proactively clarifying or making assumptions when handling ambiguous instructions.

3. Incorporate Post-Training Capabilities into Long-Term Planning

For enterprises planning to embed LLMs into specific business scenarios, consider conducting scenario-oriented post-training on top of general models. This can significantly improve the model’s performance on specific tasks without changing the base model.

It must be clear that post-training is not simply “training for a few more epochs.” It requires building high-quality environments, feedback mechanisms, and evaluation systems. Particularly for terminal agent tasks, without adequate interactive environments and automated verification systems, post-training effectiveness will be severely compromised.

4. Choose Technical Routes Based on Cost

If your business scenario is sensitive to inference latency and resource consumption, prioritizing a moderately sized model with deep post-training—provided task completion rates are not lowered—may be more practical than directly using an ultra-large base model.

Conversely, if the business needs to handle a vast array of knowledge-intensive tasks and possesses robust cloud deployment capabilities, you can consider a larger-scale base model paired with a specialized post-training pipeline.

VII. Conclusion

The two routes of “intensifying post-training without changing the base” versus “directly upgrading to a larger base” fundamentally represent different choices for enhancing model capabilities at different stages.

The former represents a more refined and pragmatic technical attitude: it acknowledges that the base model still has untapped potential and emphasizes elevating the capability ceiling through high-intensity, long-horizon, multi-environment post-training. This approach generally means lower deployment costs, a smoother upgrade path, and more controllable inference resource consumption for developers and enterprises.

The latter represents a more aggressive structural expansion: acquiring broader knowledge coverage and stronger representational capacity through larger model volumes. However, a large base model without high-quality post-training may still perform mediocrely on real-world tasks.

For the vast majority of engineering teams, the more pertinent question is not “which is stronger,” but “which route can deliver stable and reliable capability improvements faster for near-term practical tasks.” On this issue, post-training scaling has proven through a series of practical results that it is by no means a simple patch-style optimization, but a technical mainstay capable of independently supporting model capability growth.