How Large-Scale Post-Training Reshapes Model Expertise

1. From Parameter Scale to Capability Depth: Two Paths of LLM Evolution

Improvements in large model capabilities are currently advancing along two main paths.

The first path is pre-training. It focuses on model scale, architectural efficiency, and the scale and quality of training data. Over the past two years, parameter counts have rapidly grown from billions to hundreds of billions or even trillions, and open-source communities continue to release models approaching 3T parameters. The value of this direction lies in building broad world knowledge and foundational language abilities—it is the “base” of a model’s capabilities.

The second path is post-training. It includes supervised fine-tuning, reinforcement learning, and related methods. Especially over the past year or two, large-scale reinforcement learning has become a key means of improving a model’s reasoning and complex task handling. Unlike pre-training, post-training focuses more on “how to make the model perform more reliably on specific tasks” rather than simply expanding its knowledge.

One notable trend: once pre-training scale reaches a certain level, the marginal returns from continuing to stack parameters begin to diminish, while the capability gains from post-training remain significant. This has gradually turned post-training from a “nice-to-have” into a “core competitive advantage.”

2. Why Post-Training Has Become a Competitive Focus

The essence of post-training is to take a model’s existing capabilities and, through repeated trial and error along with feedback signals, turn “can do” into “does it correctly and consistently.”

Take a math problem as an example: the model gives an answer, and judging whether it is right or wrong is relatively easy. Such tasks have low verification cost and clear feedback signals, making them suitable for early reinforcement learning training.

But when we want a model to complete a complex programming task, the situation becomes far more complicated. Programming tasks often have no single correct answer, and the model needs to:

1. Understand the requirements and break down the task;
2. Modify the code;
3. Run tests;
4. Locate the problem based on error messages;
5. Modify again and verify.

This process may require many rounds of execution. The training system must provide rewards based on actual execution results and update the model accordingly. This means the effectiveness of post-training increasingly depends on one key condition: how many high-quality environments the model can use to repeatedly try, fail, and receive reliable feedback.

In other words, competition in post-training is shifting from “algorithm design” to “infrastructure and data environments.”

3. Infrastructure Challenges of Large-Scale Agent RL

To enable a model to continuously improve on verifiable tasks such as programming through reinforcement learning, a full supporting system is required. These technical terms may sound dry, but they are all important infrastructure for large-scale Agent RL.

3.1 Execution Environment: Real, Not Simulated

A model cannot write code only “in its imagination.” It needs a real execution environment that can run code, execute tests, and return error messages. This environment must provide:

• Isolation: executions of different tasks do not interfere with each other;
• Reproducibility: the same input should produce consistent results;
• Observability: logs, errors, and outputs during execution can all be collected.

3.2 Large-Scale Sandboxes and Scheduling

If you want to train tens of thousands of tasks simultaneously, you must build a large-scale sandbox and scheduling system. The scheduling system needs to address:

• How tasks are assigned to different sandboxes;
• How sandbox resources are reused and reclaimed;
• How execution timeouts and exceptions are handled;
• How to ensure overall throughput and stability.

This is essentially a distributed resource scheduling problem, closely related to general technologies such as container orchestration and task queues.

3.3 Task Quality and Evaluation Reliability

The quality of training data directly determines the upper limit of post-training. The tasks themselves need to have:

• Clear objectives and verifiable completion criteria;
• An appropriate difficulty distribution, avoiding tasks that are too easy or too hard;
• Diverse scenario coverage to prevent the model from overfitting to specific patterns.

At the same time, evaluation results must be reliable enough. If the reward signal itself is noisy, the model will learn incorrect behavior. Therefore, the design of test cases and the logic for judging execution results both need strict validation.

4. A Typical Post-Training Loop

The following simplified pseudocode illustrates the training loop of large-scale Agent RL:

for episode in range(num_episodes):
    task = sample_task(task_pool)
    sandbox = acquire_sandbox()
    state = sandbox.setup(task)
    total_reward = 0
    for step in range(max_steps):
        action = model.generate_action(state)
        result = sandbox.execute(action)
        reward = evaluate(result, task)
        total_reward += reward
        state = result.next_state
        if result.done:
            break
    model.update(total_reward)
    release_sandbox(sandbox)

This loop looks simple, but every step depends on the infrastructure mentioned earlier: the quality of the task pool, sandbox isolation and scheduling, and the reliability of execution result evaluation. If any link fails, the training signal becomes distorted.

5. Implications for Developers and Enterprises

For developers and enterprise users, this trend has several practical implications:

1. When selecting a model, post-training investment becomes an important indicator. A model’s performance on programming and complex task execution often reflects its practical usability better than parameter scale.
2. Building post-training capabilities in-house requires infrastructure first. If you want to fine-tune with reinforcement learning for your own business, execution environments, sandbox scheduling, and evaluation systems are unavoidable engineering investments.
3. General cloud capabilities can lower the barrier. General categories such as container platforms, object storage, message queues, logging services, and monitoring and alerting can provide foundational support for sandbox scheduling and execution observability, allowing teams to focus more on task design and reward modeling.
4. Focus on verifiable tasks. The areas where post-training shows the most obvious results are usually those where outcomes can be automatically verified, such as programming, mathematics, and structured data processing. These scenarios are better suited as entry points.

6. Conclusion

From pre-training to post-training, the improvement of large model capabilities is entering a new stage. Parameter scale remains the foundation, but what truly determines a model’s performance on professional tasks is the depth of post-training investment and the maturity of the infrastructure.

The core of large-scale post-training is not a single algorithmic trick, but whether it is possible to build a high-quality, scalable, and reliably feedback-driven trial-and-error environment. Whoever does this more solidly is more likely to extend a model’s accumulated capabilities in areas such as programming to more professional tasks that can be actually executed and repeatedly verified.

For developers and enterprises, understanding this trend helps both in evaluating model capabilities more rationally and in planning their own engineering investments in reinforcement learning and Agent directions.